Abstract

Efficient power conversion is an extremely important area of research in modern day electronics.Because of wide range of supply voltages in which the ICs operate, the power conversion or the power management IC (PMIC) needs to work from one voltage range and deliver power at different voltage range. Over the last 20 years, automotive industry has seen a lot of evolution from being a purely mechanical system to an electro-mechanical system. Unlike consumer electronic goods like smartphones, automotive electronics demand high quality, lifetime and durability. Most automobiles available these days run on a 12V battery. The commonly used lead-acid battery when fully charged can exhibit a voltage as high as 14V. Because of the miniaturization of image sensors, automotive rear view cameras in particular have started to find their way into middle and lower segment range automobiles. An automotive camera module needs to withstand high input voltage range and deliver multiple output voltages and load currents at very high step-down ratio. The choice of DSP, sensor and micro-controller will determine overall load current of the application. Though the camera system with PMIC will be quite low power in an automobile, it is also an extremely small board in a hot environment and hence good power efficiency becomes important. Therefore, the PMIC needs to have good efficiency not only at peak load condition but also at moderate and light load condition. Recent trends on high-voltage high step-down ratio power converters are pointing towards voltage regulator modules with high power requirements (>10W). The power transistors are discrete and only the controller is integrated into the IC. However, with increasing number of moderate power applications like automotive camera modules, there is renewed interest to look into design of low-cost power converters that can deliver moderate load power (1-3W). The power management unit (PMU) designed needs to be robust to the wide supply voltage transients and withstand high ambient temperature of 105 ◦ C. Due to self-heating of the PMU, the circuits need to be designed to be robust for IC junction temperatures close to 150 ◦ C. An affordable, reliable and highly-efficient automotive reverse camera system is found to have all the required challenges for a VLSI designer to make it a very interesting research problem for this thesis work. A combination of DC-DC (switching converters) and low-dropout (LDO) regulator, which is known as hybrid regulator, is found to give the best compromise in terms of system efficiency, output voltage ripple while keeping the overall component count to the minimum. State-of-the-art hybrid regulators have high-efficiency in a very limited load range. The LDO dropout voltage which is decided by the peak load current requirement is the main source of inefficiency at moderate and light load condition. There are also several works that deal with the light-load efficiency improvement of the switching converters but they have either low supply voltage or limited load current range. This work focusses on a generic system efficiency improvement solution based on the adaptive dropout of the LDO has been proposed. Optimal circuit design architecture with adaptive LDO dropout has been implemented to handle single DC-DC and upto three LDOs with independent voltage and current requirements. AMS 0.35μm high-voltage CMOS technology with 50V transistors and isolated 3.3V transistors have been used to design the power management circuits. An optimal two-chip PMIC solution with hybrid regulators has been proposed to meet the power supply requirements of rear-view camera application. The design of the two PMICs have been successfully completed and fabricated. The packaged ICs have been tested for their functionality. The innovation has been successfully validated in the lab and the experimental results prove the improved efficiency with respect to the conventional hybrid regulator across the 95% of the load current range and IC junction temperature upto 150 ◦ C. The design has been benchmarked also with state-of-the-art commercially available PMICs. Though a specific application like automotive reverse camera has been chosen to define the thesis goals, the novel contributions of the thesis work is very generic and can be applied to any hybrid voltage regulators with DC-DC and LDO combination.

Abstract

Short Term Load Forecasting (STLF) has always been one of the most critical, sensitive and accuracy demanding factors of the Power Systems. An accurate STLF improves not only the system’s economic viability but also its safety, stability and reliability in operation to realize futuristic Smart Power System. The research presented in this work supports the argument of hybrid approach whereby the complementary strengths of different intelligent techniques are combined to offer better solution to the problem of STLF. This work presents Artificial Intelligence (AI) and Data Mining based formulation for STLF combined with Statistical Techniques. The Temperature, Humidity and Day Type are considered as they are significant factors impacting the effectiveness of an accurate STLF. A Euclidean Norm based Similar Day Approach using the Correction Factors generated by the Fuzzy Inference System has been developed initially for the STLF and novelty is introduced in this methodology by assigning weights to various variables used in the Euclidean Norm to increase the effectiveness of various variables in deciding the similarity. The Fuzzy Inference System (FIS) is designed with three input membership functions, Load Difference (ΔE L ), Temperature Difference (ΔE T ) and Humidity Difference (ΔE H ) taking Trapezoidal Memberships and one output membership function which is the Correction Factor taking a Triangular Membership. Mamdani inference system is used for the FIS. Twenty seven rules are predefined based on the data history and these rules govern the functioning of the FIS. This Euclidean Norm based FIS is used to perform the STLF. The methodology basically uses the Euclidean Norm to select the few similar days of the forecast day from the historic dataset. These similar days of the forecast day are then corrected using the correction factors generated by the FIS that uses the forecast previous day and its similar days’ hourly load, hourly temperature and hourly humidity differences as the input to the FIS. The average of the corrected hourly loads of the similar days of the forecast day is then considered as the hourly load of the forecast day. The Euclidean Norm based FIS has been tested on a dataset of 7 months data. Further to this the input parameter limits of the FIS have been optimized using three Swarm Intelligence Techniques namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The optimized PSO-FIS, NPSO-FIS and EPSO-FIS were used to perform the STLF on a 7 months dataset as well as data of 3 years as historic dataset. The results of all the three techniques were found to be good. Further another novel methodology, which amalgamates the Clustering Technique of Data Mining with the Regression Technique, has been developed in this thesis to give a more accurate STLF. The framework of the Clustering based Regression methodology is detailed as follows: to forecast the load of a given day, the forecast day and the similar days (all Mondays for Monday and likewise) of the forecast day selected from the previous two years history data are clustered using the k- medoids clustering algorithm, based on their similarity of weather variables i.e. Temperature and Humidity. Further, the proposed method finds the Regression constants from the cluster matrix containing the forecast day. The load forecasting for the forecast day is then carried out using the Regression Equation. The zest of this technique is that clustering brings together the very similar days of the forecast day in one cluster and regression technique further encapsulates the total correlation of load and weather variables of the similar days in the cluster, hence enhancing the forecast efficiency. Historical data set, which captures the impact of weather variables on the load, is used for simulation studies and the proposed technique has been found very successful for load forecast of all days and all seasons. The research presented in this thesis also deals with the very important issue of STLF for special days. This research presents a Hybrid Data Mining based formulation of STLF with emphasis on special days and anomalous days, such as public and national holidays, which are often ignored during the general modeling process. The methodology proposed in present research generates STLF for special days using an Artificial Intelligence based formulation combined with Statistical Techniques. The methodology basically uses the Clustering technique of data mining and combines it with the Regression technique with a variation in the selection of days in historic data set. The proposed framework is detailed as follows:

Abstract

This thesis proposes new signal processing methods to highlight some robust speech-specific features present in the degraded speech. It considers different types of degradations that occur in practice. The signal processing methods are based on single frequency filtering (SFF) of speech signal. The SFF output gives magnitude or envelope and the phase of the speech signal at any desired frequency with high frequency resolution. The SFF output at each frequency gives some segments with high signal-to-noise ratio (SNR), as the noise power in the near zero bandwidth resonator of the single frequency will be very small, whereas the signal component, if it exists, will have high power. Thus the high SNR regions will be at different times for different frequencies. This property of the SFF analysis of speech is exploited for extracting a few robust features from the degraded speech, irrespective of the type and extent of degradation. In particular, the following studies are carried out in this thesis: - Discrimination of speech/nonspeech regions in degraded speech - Determination of speech regions in speech degraded by transient noise - Extraction of the fundamental frequency from degraded speech - Detection of glottal closure instants (GCIs) in degraded speech - Enhancement of degraded speech The major contributions of this work are the following: (a) A new signal processing method called single frequency filtering (SFF) method is proposed which gives high signal-to-noise ratio (SNR) regions in both time and frequency domains for speech affected with different types of degradations. (b) A new method for speech/nonspeech detection is proposed exploiting the high SNR features in the SFF outputs of degraded speech. The procedure works for all types of degradations, without specifically tuning for any specific type of degradation. (c) The high SNR characteristic of the SFF output is also exploited for estimating the fundamental frequency ( f o ) by exploiting information at the frequency that gives the highest SNR for that segment. (d) The noise compensation technique proposed for voice activity detection (VAD) is applied for extracting the location of the significant impulse-like excitation within a glottal cycle. This is because the noise compensated envelopes show distinct changes in the slope of the spectral variance computed as a function of time. (e) The noise compensated SFF envelopes derived at different frequency resolutions are used to derive gross and fine weight functions, as a function of time. The combined weight functions when applied to the degraded speech signal produces enhanced speech for speech affected by different types of degradations, thus improving the comfort level of listening.

Abstract

Sentence Classification is one of the most fundamental tasks in Natural Language Processing (NLP), wherein the aim is to classify any given sentence into one of a given set of classes. The set of classes usually depends upon the specific task under consideration, such as determining the type of answer in Question Classification or finding the sentiment of a sentence in Sentiment Analysis or finding whether a sentence is subjective or objective in Subjectivity Analysis. These are some of the tasks that can be included in Sentence Classification. Some examples of other tasks being sarcasm or humour detection, detection of abusive text etc. In sentence classification, the aim is to have a common network architecture for a range of classification tasks, rather than building an independent one for each task. In other words, the aim is to build a task-independent classification system. Many methods have been developed for various sentence classification tasks for English, which usually exploit linguistic resources like parsers making it difficult to adapt them to other languages. There are a huge number of languages spoken around the world, and it is not possible to build an independent classifier for each task in every language. Thus, in this thesis, we explore language-independent task-independent sentence classification focusing on improvement in classification of morphologically rich languages. We perform sentence classification for five datasets in two tasks - Sentiment Analysis and Question Classification and three languages - English, Hindi and Telugu. We present an evaluation of popular deep learning methods for sentence classification on the morphologically rich Indian languages, specifically Hindi and Telugu. For this purpose, we also created a question classification dataset for Hindi, by translating the TREC-UIUC English dataset. We show that character based input can enhance the performance of current classification systems for morphologically rich languages. Finally, we show that our proposed multiInput-CNN variant is able to perform better than our baselines in two out of three tasks in Hindi and Telugu, while giving comparable results for others. Since a huge proportion of the work in sentence classification is specifically for English, the methods are often designed to best suit that language. A lot of the works represent the input sentences as asequence of words in their models. Only a few of them rely on character level representation. Through this work, we introduce a new method for representing a sentence - as a sequence of syllables. It is essential to capture sub-word level information while classifying in a morphologically rich language. Syllables are an ideal choice for this, as in many languages most of the morphemes can be seen as n-grams of syllables. Thus, making them a more intuitive choice when compared with character n-grams to capture morphological information. Also, syllables can lead to a reduction in noise caused due to the usage of character n-grams. Through extensive evaluation, we show that syllables are the best performing input type when compared to words or characters for the morphologically rich languages - Hindi and Telugu. Finally, we make use of attention mechanism to automatically find the best combination of inputs for every dataset and language. We experiment with a number of different attention based CNN models in this part and find that attention can be used to obtain a model that performs decently well across all datasets.

Abstract

Research in Natural Language Processing is expanding in multiple domains and is seeping into all aspects of life with time. With every advancement, the variety of text that can be processed is growing. One such domain is lyrics processing. Songs are vital to the music and film industry and can be analysed to obtain important information such as genre, theme, mood, etc. of the song and supplement the information gathered by the study of its audio features. Bollywood, the Indian film industry makes a lot of revenue making use of songs. The number of songs churned out by this industry is massive and is a rich source of audio and textual data for Natural Language Processing tasks. It also gives us an opportunity to work on data in Hindi which is a relatively less explored field. The focus of this thesis is on the textual part of the data. In an attempt to create a data resource for this domain, this work presents a corpus of Bollywood song lyrics and its metadata, annotated with sentiment polarity. We call this BolLy. It contains lyrics of 1055 songs ranging from those composed in the year 1970 to the most recent ones. This dataset is of utmost value as all the annotation is done manually by three annotators and this makes it a very rich dataset for training purposes. In this work, we describe the creation and annotation process, content, and the possible uses of the dataset. As an experiment, we have built a basic classification system to identify the emotion polarity of the song based solely on the lyrics and this can be used as a baseline algorithm for the same. The lyrics of Hindi songs have been used for their classification as having positive or negative sentiment by extraction of opinions. Some experiments employing subjectivity lexicons and probabilistic approaches were conducted for this on a dataset which was just a subset of ‘BolLy’. This motivated us to work with a bigger dataset and focus our efforts towards expanding it. With the complete ‘BolLy’ dataset, the experiments have been created using different variations of the Naive Bayes Classifier. There can be a multitude of Natural Language Processing applications on the presented dataset. This thesis work contains one of them explored in detail. Keywords of a document are a representative of its content, and it helps to have meaningful words to facilitate search and organization of documents. Hence, finding methods that can automatically identify keywords in a document is very important as manual processes for this is very cumbersome and error-prone.If this task is accomplished for song lyrics, it has varied applications such as recommendation systems and digital music library management. This work proposes and compares methods to identify keywords from lyrics of Bollywood songs. We use a collection of lyrics of 1055 Bollywood songs, all written in the Devanagari script. Experiments include looking at the spatial distribution of the terms, their occurrence in a certain context or position, and using WordNet to generate keywords not present in the document. Validation was done by human annotators by providing a score to each method based on the results obtained on a subset of the data. We also used Latent Dirichlet Allocation and Latent Semantic Indexing to validate the results, as further explained in the paper.

Abstract

Many socio-environmental aspects manifest themselves over space and time, interacting at varying scales of these dimensions. Satellite imagery, available repetitively over a region, provide important clues of these observations across these dimensions. But, also pose enormous challenges in terms of data processing, extracting significant patterns (indicating the underlying processes) and be able to fur- ther model them as scientific knowledge of the environmental process. Study of cropping practices and changes occurring in it over time for a region is critical to the field of agronomics. The increase of temporal resolution of Earth Observation Satellite platforms has re- sulted in easier monitoring of the Earth’s surfaces. Satellite imagery is an exemplary data source that captures the vegetation responses of the field and such dataset can be used to effectively monitor and track vegetation health over time. The need for large-scale and structured analysis of satellite data for agricultural monitoring is growing. One of the current challenges in understanding agricultural prac- tices is to build a history of land-use outcomes and extracting the local phenological responses that help in building field-based inventory and understanding gross estimate of various cropping systems. Also, monitoring of vegetation health and identification of the major changes in crop practice described here as change-event can provide valuable insights into the possible factors that were responsible for such an event be it biological, physical, hydrological or climatic. In this study, an effort has been made to propose a time-variant analysis method based on the shape characteristics of the vegetation response over time to help identify regions of significant changes. This study attempts to build a spatio-temporal library of vegetation practices and presents an approach, supervised and unsupervised, to process the raw data obtained from the satellite images into representative phenological growth curves. The statis- tics contributed by the current work is critical to understanding and assessing the changes in cropping practices over time and detecting possible drivers that triggered to cause such change-events. The study covers four agricultural-year periods between 2008 and 2012 over the district of West Godavari, in the south of India. An interesting result from this study was to observe the impact of drought on cropping cycles during and after the drought year for the given region. The work finds that the immediate effects of drought in a given year are limited if the region is well endowed with other resources such as irrigation facility etc. Whereas its impacts on cropping patterns can also be seen in the season following the drought year. And such changes vary across the district depending on where vi vii cropping is spatially located. The approach uncovers that the effect of the 2009 drought year on the agricultural practices vary spatially depending on the access to resources and the time-lag that manifests itself in such processes. In this study, we also find that nearly 80% of the region is well endowed and hence resilient to the climatic vagaries. The major contribution of this work is the analysis of spatio-temporal data to detect changes that may help in capturing episodic and periodic change-events occurring in crop cycles of any given region. With vast data spanning across multiple regions in hand, the proposed shape-based approach that characterize the phenological nature of these time-dependent sequences help to discover regions that are spatially correlated and find crop practices that are prevalent across similarly endowed regions.

Abstract

The amelioration in video capture technology has made recording videos very easy. The introduction of smaller affordable cameras which not only boast high-end specifications but are also capable of capturing videos at very high resolution (4K,8K and even 16K) have made recording of high quality videos accessible to everyone. Although this makes recording of videos very straightforward and effortless, a major part of the video production process - Editing is still labor intensive and requires skill and expertise. This thesis takes a step towards automating the editing process and making it less time consuming. In this thesis, (1) we explore a novel approach of automatically editing stage performances such that both the context of the scene as well as close-up details of the actors are shown. (2) We propose a new method to optimally re-target videos to any desired aspect ratio while retaining the salient regions that are derived using gaze tracking. Recordings of stage performances are easy to capture with a high-resolution camera, but are difficult to watch because the actors’ faces are too small. We present an approach to automatically create a split screen video that transforms these recordings to show both the context of the scene as well as close-up details of the actors. Given a static recording of a stage performance and the tracking information about the actors positions, our system generates videos showing a focus+context view based on computed close-up camera motions using crop-and zoom. The key to our approach is to compute these camera motions such that they are cinematically valid close-ups and to ensure that the set of views of the different actors are properly coordinated and presented. We pose the computation of camera motions as convex optimization that creates detailed views and smooth movements, subject to cinematic constraints such as not cutting faces with the edge of the frame. Additional constraints link the close up views of each actor, causing them to merge seamlessly when actors are close. Generated views are placed in a resulting layout that preserves the spatial relationships between actors. This eliminates the need for manual labour and expertise required for both capturing the performance and later editing it, instead the splitscreen of focus+context views allows the viewer to make an active decision on attending to whatever seems important. We also demonstrate our results on a variety of staged theater and dance performances. When videos are captured they are captured according to a specific aspect ratio keeping in mind the size of target screen in which they are meant to be viewed, this results in a inferior viewing experience when they are not watched on screens with their native aspect ratio. We present an approach to auto-matically retarget any given video to any desired aspect ratio while preserving its most salient regions obtained using gaze tracking. Our algorithm performs editing with cut, pan and zoom operations by optimizing the path of a cropping window within the original video while seeking to (i) preserve salient regions, and (ii) adhere to the principles of cinematography. The algorithm has two steps in total. The first step uses dynamic programming to find a cropping window path that maximizes gaze inclusion within the window and also tries to find the location of plausible new cuts (if required). The second step performs regularized convex optimization on the path obtained via dynamic programming to produce a smooth cropping window path comprised of piecewise linear, constant and parabolic segments. We test our re-editing algorithm on a diverse collection of movie and theater sequences. A study conducted with 16 users confirms that our retargeting algorithm results in a superior viewing experience as compared to gaze driven re-editing [30] and letterboxing methods, especially for wide-angle static camera recordings.

Abstract

The amelioration in video capture technology has made recording videos very easy. The introduction of smaller affordable cameras which not only boast high-end specifications but are also capable of capturing videos at very high resolution (4K,8K and even 16K) have made recording of high quality videos accessible to everyone. Although this makes recording of videos very straightforward and effortless, a major part of the video production process - Editing is still labor intensive and requires skill and expertise. This thesis takes a step towards automating the editing process and making it less time consuming. In this thesis, (1) we explore a novel approach of automatically editing stage performances such that both the context of the scene as well as close-up details of the actors are shown. (2) We propose a new method to optimally re-target videos to any desired aspect ratio while retaining the salient regions that are derived using gaze tracking. Recordings of stage performances are easy to capture with a high-resolution camera, but are difficult to watch because the actors’ faces are too small. We present an approach to automatically create a split screen video that transforms these recordings to show both the context of the scene as well as close-up details of the actors. Given a static recording of a stage performance and the tracking information about the actors positions, our system generates videos showing a focus+context view based on computed close-up camera motions using crop-and zoom. The key to our approach is to compute these camera motions such that they are cinematically valid close-ups and to ensure that the set of views of the different actors are properly coordinated and presented. We pose the computation of camera motions as convex optimization that creates detailed views and smooth movements, subject to cinematic constraints such as not cutting faces with the edge of the frame. Additional constraints link the close up views of each actor, causing them to merge seamlessly when actors are close. Generated views are placed in a resulting layout that preserves the spatial relationships between actors. This eliminates the need for manual labour and expertise required for both capturing the performance and later editing it, instead the splitscreen of focus+context views allows the viewer to make an active decision on attending to whatever seems important. We also demonstrate our results on a variety of staged theater and dance performances. When videos are captured they are captured according to a specific aspect ratio keeping in mind the size of target screen in which they are meant to be viewed, this results in a inferior viewing experience when they are not watched on screens with their native aspect ratio. We present an approach to automatically retarget any given video to any desired aspect ratio while preserving its most salient regions obtained using gaze tracking. Our algorithm performs editing with cut, pan and zoom operations by optimizing the path of a cropping window within the original video while seeking to (i) preserve salient regions, and (ii) adhere to the principles of cinematography. The algorithm has two steps in total. The first step uses dynamic programming to find a cropping window path that maximizes gaze inclusion within the window and also tries to find the location of plausible new cuts (if required). The second step performs regularized convex optimization on the path obtained via dynamic programming to produce a smooth cropping window path comprised of piecewise linear, constant and parabolic segments. We test our re-editing algorithm on a diverse collection of movie and theater sequences. A study conducted with 16 users confirms that our retargeting algorithm results in a superior viewing experience as compared to gaze driven re-editing [30] and letterboxing methods, especially for wide-angle static camera recordings.

Abstract

Social media platforms like Twitter and Facebook have become two of the largest platforms for people to communicate and share their views with the people. The casual and informal environment on these platforms leads to more people expressing themselves in their native language which results in a larger amount of code-mixed data that the annotated set of data currently lacks. With access to public opinion on nearly every topic, we can gather a huge amount of user data which could prove to be useful for various companies, thus making tasks like opinion mining and sentiment analysis even more important. Hence understanding users in low resourced languages has become one of the most researched tasks of late. We present two English-Hindi code-mixed datasets and to evaluate these datasets we simultaneously build baseline classification systems to evaluate them. As it takes time to create the datasets we decided to test our classification system on another dataset of Spanish and Catalan tweets on Catalan Independence as Catlan is one of the low resourced languages when seen from the perspective of Natural Language Processing. Thus, we first present a supervised classification system for stance and gender detection in Spanish and Catalan tweets on Catalan Independence. Then we present two English-Hindi code-mixed corpus, one for stance detection and the other for sarcasm detection in code-mixed tweets. The tweets for stance detection are collected for the target ‘Demonetisation’ whereas the tweets for sarcasm detection are collected on various topics such as cricket, bollywood, and politics. Each tweet in their respective datasets is marked for the stance and presence of sarcasm. Each token in the tweets is annotated with a language tag. Finally, we present a classification system developed using these datasets for stance and sarcasm detection. This system uses various word and character level features along with three different classification techniques. 10-fold cross-validation is used for evaluation of this system.

Abstract

Over the last decade, there has been an exponential increase in the popularity of social media platforms such as Facebook, Twitter and other online social networking websites. Apart from offering a fast, easy and reliable method of communication with friends and family, for many people they have also become the primary source of information on current events and happenings around the world. With the rise of these social media networks, it has become extremely easy for anyone and everyone to share text, images and videos with other people. It has also proven to be a boon for creative people, by allowing them to share their work with a lot of people instantly at the press of a button. The advantages of these online social networks are manifold. Unfortunately, some characteristics of user generated content (UGC) poses problems in the way of natural language processing of the data that is shared by users on these websites. UGC typically contains non-standard spellings of words, unusual abbreviations, informal grammatical structure and variations in vocabulary, which are all issues that are not easily mitigated and therefore render current systems ineffective in processing such information. In recent years, a phenomenon called “code-mixing”, which is frequently observed in online social media, has attracted a lot of research and interest from sociolinguists. A sentence is said to be codemixed if it consists of linguistic units, such as phrases, words or morphemes, from multiple languages. Code-mixing is often observed in the utterances of multilingual speakers, and is prevalent in countries where people speak multiple languages with native proficiency such as India. The most prominent effect of code-mixing on everyday life can be seen in UGC on online social networks. Due to the informal nature of UGC, code-mixing is bound to happen especially in text written by multilingual writers. Since this hybrid code-mixed language does not have a formally defined grammar, it becomes a challenging task to process this information and analyze it further. In this thesis, we attempt to understand the phenomenon of code-mixing in a deeper manner by analyzing text from online social media platforms. For deep learning NLP approaches to work on code-mixed text, they require an abundance of appropriate data to train on. Currently, there is a lack of such resources, therefore we start this work with the creation of 6096 English-Hindi code-mixed to monolingual English parallel corpus, which could aid deep learning methods in processing codemixed text. We then proceed to develop a pipeline that can augment existing Machine Translation (MT) systems to enable support for translating code-mixed languages to monolingual. We present the results obtained by experiments conducted that augmented existing statistical and neural network based machine translation systems such as Google Translate, Bing and Moses. This pipeline was also used toaugment the classification of sentiments for code-mixed sentences by existing systems, by translating code-mixed English-Hindi sentences to English using the augmented MT systems and then using an available sentiment analysis system for English. We take our work on code-mixing further by developing a hybrid architecture model that relies on neural networks with Attention mechanism as well as linguistic features to classify the sentiment labels for code-mixed sentences collected from online social networks such as Facebook and Twitter. We report these results which show that our approach is able to outperform previous methods for this task. Finally, we demonstrate that a similar hybrid architecture can be used for other NLP tasks as well, such as Clickbait detection and Aggression identification of online social media posts. Our approach imporved on the results obtained by previous methods for the task of clickbait detection. The results of these experiments are also presented in this thesis.