In this thesis we have present effective approaches for two classic Nature Language Processing tasks: Multi-label Text Classification(MLTC) and Sentiment Analysis(SA) based on two datasets. For MLTC, a robust deep learning approach based on convolution neural network(CNN) has been introduced. We have done this on almost one million records with a related label list consists of 20 labels. We have divided our data set into three parts, training set, validation set and test set. Our CNN based model achieved great result measured in F1 score. For SA, data set was more informative and well-structured compared with MLTC. A traditional word embedding method, Word2Vec was used for generating word vector of each text records. Following that, we employed several classic deep learning models such as Bi-LSTM, RCNN, Attention mechanism and CNN to extract sentiment features. In the next step, a classification frame was designed to graded. At last, the start-of-art language model, BERT which use transfer learning method was employed. In conclusion, we compared performance of RNN-based model, CNN-based model and pre-trained language model on classification task and discuss their applicability. / Thesis / Master of Science in Electrical and Computer Engineering (MSECE) / This theis purposed two deep learning solution to both multi-label classification problem and sentiment analysis problem.

This dissertation focuses on the review understanding problem, which has gained attention from both industry and academia, and has found applications in many downstream tasks, such as recommendation, information retrieval and review summarization. In this dissertation, we aim to develop machine learning and natural language processing tools to understand and learn structured knowledge from unstructured reviews, which can be investigated in three research directions, including understanding review corpora, understanding review documents, and understanding review segments. For the corpus-level review understanding, we have focused on discovering knowledge from corpora that consist of short texts. Since they have limited contextual information, automatically learning topics from them remains a challenging problem. We propose a semantics-assisted non-negative matrix factorization model to deal with this problem. It effectively incorporates the word-context semantic correlations into the model, where the semantic relationships between the words and their contexts are learned from the skip-gram view of a corpus. We conduct extensive sets of experiments on several short text corpora to demonstrate the proposed model can discover meaningful and coherent topics. For document-level review understanding, we have focused on building interpretable and reliable models for the document-level multi-aspect sentiment analysis (DMSA) task, which can help us to not only recover missing aspect-level ratings and analyze sentiment of customers, but also detect aspect and opinion terms from reviews. We conduct three studies in this research direction. In the first study, we collect a new DMSA dataset in the healthcare domain and systematically investigate reviews in this dataset, including a comprehensive statistical analysis and topic modeling to discover aspects. We also propose a multi-task learning framework with self-attention networks to predict sentiment and ratings for given aspects. In the second study, we propose corpus-level and concept-based explanation methods to interpret attention-based deep learning models for text classification, including sentiment classification. The proposed corpus-level explanation approach aims to capture causal relationships between keywords and model predictions via learning importance of keywords for predicted labels across a training corpus based on attention weights. We also propose a concept-based explanation method that can automatically learn higher level concepts and their importance to model predictions. We apply these methods to the classification task and show that they are powerful in extracting semantically meaningful keywords and concepts, and explaining model predictions. In the third study, we propose an interpretable and uncertainty aware multi-task learning framework for DMSA, which can achieve competitive performance while also being able to interpret the predictions made. Based on the corpus-level explanation method, we propose an attention-driven keywords ranking method, which can automatically discover aspect terms and aspect-level opinion terms from a review corpus using the attention weights. In addition, we propose a lecture-audience strategy to estimate model uncertainty in the context of multi-task learning. For the segment-level review understanding, we have focused on the unsupervised aspect detection task, which aims to automatically extract interpretable aspects and identify aspect-specific segments from online reviews. The existing deep learning-based topic models suffer from several problems such as extracting noisy aspects and poorly mapping aspects discovered by models to the aspects of interest. To deal with these problems, we propose a self-supervised contrastive learning framework in order to learn better representations for aspects and review segments. We also introduce a high-resolution selective mapping method to efficiently assign aspects discovered by the model to the aspects of interest. In addition, we propose using a knowledge distillation technique to further improve the aspect detection performance. / Doctor of Philosophy / Nowadays, online reviews are playing an important role in our daily lives. They are also critical to the success of many e-commerce and local businesses because they can help people build trust in brands and businesses, provide insights into products and services, and improve consumers' confidence. As a large number of reviews accumulate every day, a central research problem is to build an artificial intelligence system that can understand and interact with these reviews, and further use them to offer customers better support and services. In order to tackle challenges in these applications, we first have to get an in-depth understanding of online reviews. In this dissertation, we focus on the review understanding problem and develop machine learning and natural language processing tools to understand reviews and learn structured knowledge from unstructured reviews. We have addressed the review understanding problem in three directions, including understanding a collection of reviews, understanding a single review, and understanding a piece of a review segment. In the first direction, we proposed a short-text topic modeling method to extract topics from review corpora that consist of primary complaints of consumers. In the second direction, we focused on building sentiment analysis models to predict the opinions of consumers from their reviews. Our deep learning models can provide good prediction accuracy as well as a human-understandable explanation for the prediction. In the third direction, we develop an aspect detection method to automatically extract sentences that mention certain features consumers are interested in, from reviews, which can help customers efficiently navigate through reviews and help businesses identify the advantages and disadvantages of their products.

Selecting the correct methodology to better understand how consumers perceive food products is a challenging task for the food industry and sensory researchers alike. Free comment tasks (FC) utilize the advantages of open-ended questions to generate intuitive comments from untrained consumers to help identify and describe sensory attributes of products. However, FC data is typically analyzed using text analysis done by hand and is very cumbersome to organize and interpret. There is a growing need and interest to add to the library of data analysis tools used to understand FC data and consumer acceptance studies. Sentiment analysis is an opinion mining tool commonly used in marketing and computer science that extracts the emotional valence of the author from an unstructured text in the form of a sentiment score. A few studies in sensory evaluation use lexicon-based sentiment analysis which has many drawbacks: it is time-consuming, requires a large amount of data and dictionaries need to be tailored for food. We used a deep learning sentiment analysis approach to analyze and predict consumer sentiment/acceptance. The research objectives of this study are 1) to explore quicker and automated methods of sentiment analysis to better understand and predict consumer acceptance, and 2) to examine the advantages and disadvantages of sentiment analysis as a data analysis tool in sensory evaluation. We avoided the pitfalls of creating a sentiment lexicon by using online beer reviews to train a word embedding model where all of the relevant words in the review are converted into vectors. We used the distance and similarity (clustering) of the vectors to determine taste/flavor attributes that correspond to negative and positive sentiment. Next, to validate and test our model we gathered FC data in a consumer acceptance study. Panelists (N=68) were presented with six beers, one at a time and were instructed to taste and smell before leaving comments. We performed sentiment analysis on the FC data, and we compared our deep learning sentiment analysis model with three other pre-existing sentiment analysis models: SentimentR, VADER, and Liu and Hu opinion lexicon. Our deep learning sentiment analysis model had the highest accuracy (69%) and precision rate (73%). Overall, our findings provide an early look into the advantages and disadvantages of sentiment analysis applied to FC data in sensory evaluation. / Master of Science in Life Sciences / It can be a challenging task for the food industry and sensory researchers to select the correct methodology to better understand how consumers perceive food products. One method is Free comment tasks (FC) which use open-ended questions to generate comments from consumers. However, FC data is typically analyzed using text analysis done by hand and is very cumbersome to organize and interpret. This thesis is interested in investigating the application of data analysis methods from computer science on FC data. Sentiment analysis is an opinion mining tool commonly used in marketing and computer science that finds the emotional tone of the author from a text in the form of a sentiment score. First, we created a deep learning sentiment analysis model which uses algorithms to find useful patterns in the text that indicate positive and negative sentiment with minimal human intervention. We were interested if there were any advantages in creating a model so we compared our model to three widely used sentiment analysis models: VADER, Liu Hu and Sentiment R. Next, to test our sentiment analysis model and the three others we gathered FC data in a consumer acceptance study. Panelists (N=68) were presented with six beers, one at a time and were instructed to taste and smell before leaving comments. The research objectives of this study are 1) to explore quicker and automated data analysis methods to understand FC data, and 2) to examine the advantages and disadvantages of data analysis tools from computer science in sensory evaluation. Our deep learning sentiment analysis model had the highest accuracy (69%) and precision rate (73%). Overall, our findings provide an early look into the advantages and disadvantages of sentiment analysis applied to FC data in sensory evaluation.

An important means for disseminating information on social media platforms is by including URLs that point to external sources in user posts. In X, formally known as Twitter, we estimate that about 21% of the daily stream of English-language posts contain URLs. Given this prevalence, we assert that studying URLs in social media holds significant importance as they play a pivotal part in shaping the flow of information and influencing user behavior. Examining hyperlinked posts can help us gain valuable insights into online discourse and detect emerging trends. The first aspect of our analysis is the study of users' intentions behind including URLs in social media posts. We argue that gaining insights about the users' motivations for posting with URLs has multiple applications, including the appropriate treatment and processing of these posts in other tasks. Hence, we build a comprehensive taxonomy containing the various intentions behind sharing URLs on social media. In addition, we explore the labeling of intentions via the use of crowdsourcing. In addition to the intentions aspect of hyperlinked posts, we analyze their structure relative to the content of the web documents pointed to by the URLs. Hence, we define, and analyze the segmentation problem of hyperlinked posts and develop an effective algorithm to solve it. We show that our solution can benefit sentiment analysis on social media. In the final aspect of our analysis, we investigate the emergence of news outlets posing as local sources, known as "pink slime", and their spread on social media. We conduct a comprehensive study investigating hyperlinked posts featuring pink slime websites. Through our analysis of the patterns and origins of posts, we discover and extract syntactical features and utilize them for developing a classification approach to detect such posts. Our approach has achieved an accuracy rate of 92.5%. / Computer and Information Science

With the rapid development of technology, software applications have become essential in people's daily lives. The number of digital platforms (e.g., website and mobile) available is continuously growing, and so are the persuasive designs that impact user's experience and decision-making in an online environment. Deceptive patterns, also known as dark patterns, refer to user interface (UI) design choices crafted to manipulate or trick users into actions that they are not intended to do in digital environments. These patterns, found everywhere in digital interfaces, exploit users' psychological vulnerability and manipulate them into actions that benefit stakeholders at the expense of users' interests. To bring more awareness of the dark patterns, scholarship on the topic is vastly increasing. However, there is limited study on how dark patterns impact users' perceptions and interaction with applications. Furthermore, work has yet to investigate dark patterns from the perspective of software engineers, the developers who implement user interface designs. To that end, our study seeks to explore users' and developers' perspectives on dark patterns In this study, we used a mixed-method approach, surveying each stakeholder group (N_user=66 and N_developer=38) and mining GitHub data (N=2556) to understand end users' perceptions and experiences and developers' discussions and attitudes about dark patterns. Our findings reveal that users often encounter dark patterns online with limited options for avoidance, which evoke negative emotions. Developers report that external pressures influence their decisions to implement dark patterns, and most recognize their adverse effects on trust and user experience. Discussions on GitHub primarily focus on the existence and prevention of dark patterns, often reflecting negative sentiments. With our findings, we aim to raise stakeholders' awareness of dark patterns and promote ethical UI design to mitigate the use of deceptive designs in online environments. / Master of Science / As technology becomes more integral to our daily lives, more digital platforms, such as websites and mobile apps, are being developed. Unfortunately, some designs manipulate users into making choices they did not mean to, like easy sign-up with a one-click button but hard to unsubscribe. These are known as ``dark patterns'' — user interface tricks that take advantage of how people think or behave online, benefiting companies at the users' expense. While research on these deceptive designs is increasing, there is little information on how they affect users or what developers think about them. For this study, we investigated how users and developers perceive dark patterns in online environments. We surveyed 66 users and 38 developers and analyzed over 2,556 discussions from open-source coding platforms like GitHub, a popular code hosting platform for open-source projects. Our findings reveal that users frequently encounter dark patterns online, which can lead to negative emotions and provide few alternatives to avoidance. A minority of developers admit to implementing dark patterns due to external pressures, while most recognize their harmful impact on trust and user experience. GitHub discussions primarily focus on the existence and prevention of dark patterns, often reflecting negative sentiments like stress and frustration.

Yes / The governments, nowadays, throughout the world are increasingly becoming dependent on public opinion regarding the framing and implementation of certain policies for the welfare of the general public. The role of social media is vital to this emerging trend. Traditionally, lack of public participation in various policy making decision used to be a major cause of concern particularly when formulating and evaluating such policies. However, the exponential rise in usage of social media platforms by general public has given the government a wider insight to overcome this long pending dilemma. Cloud-based e-governance is currently being realized due to IT infrastructure availability along with mindset changes of government advisors towards realizing the various policies in a best possible manner. This paper presents a pragmatic approach that combines the capabilities of both cloud computing and social media analytics towards efficient monitoring and controlling of governmental policies through public involvement. The proposed system has provided us some encouraging results, when tested for Goods and Services Tax (GST) implementation by Indian government and established that it can be successfully implemented for efficient policy making and implementation.

Yes / The online shopping is increasing rapidly because of its convenience to buy from home and comparing products from their reviews written by other purchasers. When people buy a product, they express their emotions about that product in the form of review. In Indian context, it is found that the reviews contain Hindi text along with English. It is also found that most of the Hindi text contains opinionated words like bahut achha, bakbas, pesa wasool etc. We have tried to find out different Hindi texts appearing in product reviews written on Indian E-commerce portals. We have also developed a system which takes all those reviews containing Hindi as well as English texts and find out the sentiment expressed in that review for each attribute of the product as well as a final review of the product.

Reddit studies have examined community formation and collective identity on individualsubreddits, analyzed the migration of users among subreddits, or provided activity metrics across the platform. Fewer studies have examined Reddit affordances on sub-community and topical levels. In my dissertation, I introduce the concept of ‘peripheral subreddits’, which are offshoots of more prominent subreddits, by studying peripheral subreddits of the GameStop movement, which initially occurred on r/WallStreetBets (r/Superstonk, r/GME, r/GMEJungle, and r/DDintoGME). I argue that analyzing the communicative activity occurring on peripheral subreddits may help communication scholars understand the growth of emerging movements that are anonymously social and ‘digital-first’. Specifically, I examine peripheral subreddits by focusing on three characteristics: topic inheritance (the provision of content themes from a more popular root subreddit), topic similarity (the shared interests among peripheral subreddits), and topic connectivity (the explicit or implicit associations among peripheral subreddits in the form of shared dialogue, activities, beliefs, or sentiments). I use computational methods such as topic modeling and sentiment analysis to analyze user activity and posts in these peripheral subreddits. Further, following the literature on digitally mediated stock market communities, I examine whether these peripheral subreddits engage in communicative processes such as aestheticization, virtualization, and de-realization, and reflexivities such as performativity, transactionality, and gamification. / Media & Communication

Throughout history, the Black Diaspora has used fashion as a form of protest. The element of fashion is often overlooked when considering the history and struggle for Black equality, because it is less tangible or definable in terms of its influence and effect, but it is still important because Black males resist the dominant culture via dress by dressing in military uniforms, creating their own style, and using different colors in their dress. Studying the Black struggle in American history during specific periods is one way to better understand opposition to the majority culture through fashion. We should also consider the mood of a social system when examining the dress of a particular group during conflicts. Hence, the purpose of this study is to investigate the role of fashion as a protest tool against the majority culture, and the social mood that affects the fashion choices of Black males. The study focuses on Black fashion from 1910 to 2015. Text data were collected and analyzed from articles published in The Crisis magazine, and men's fashion was specially examined. Additionally, images were studied via visual ethnography and images were coded based on color choice, fit, and accessories. For conducting sentiment analysis, lexicons were used, and the text was examined for negative sentiment. The overall negative sentiment of the document was obtained. Graphical analyses are included to present the findings. The findings, conclusions, limitations, and future research are discussed.

Background: Twitter, Facebook, WordPress, etc. act as the major sources of information exchange in today's world. The tweets on Twitter are mainly based on the public opinion on a product, event or topic and thus contains large volumes of unprocessed data. Synthesis and Analysis of this data is very important and difficult due to the size of the dataset. Sentiment analysis is chosen as the apt method to analyse this data as this method does not go through all the tweets but rather relates to the sentiments of these tweets in terms of positive, negative and neutral opinions. Sentiment Analysis is normally performed in 3 ways namely Machine learning-based approach, Sentiment lexicon-based approach, and Hybrid approach. The Machine learning based approach uses machine learning algorithms and deep learning algorithms for analysing the data, whereas the sentiment lexicon-based approach uses lexicons in analysing the data and they contain vocabulary of positive and negative words. The Hybrid approach uses a combination of both Machine learning and sentiment lexicon approach for classification. Objectives: The primary objectives of this research are: To identify the algorithms and metrics for evaluating the performance of Machine Learning Classifiers. To compare the metrics from the identified algorithms depending on the size of the dataset that affects the performance of the best-suited algorithm for sentiment analysis. Method: The method chosen to address the research questions is Experiment. Through which the identified algorithms are evaluated with the selected metrics. Results: The identified machine learning algorithms are Naïve Bayes, Random Forest, XGBoost and the deep learning algorithm is CNN-LSTM. The algorithms are evaluated with respect to the metrics namely precision, accuracy, F1 score, recall and compared. CNN-LSTM model is best suited for sentiment analysis on twitter data with respect to the selected size of the dataset. Conclusion: Through the analysis of results, the aim of this research is achieved in identifying the best-suited algorithm for sentiment analysis on twitter data with respect to the selected dataset. CNN-LSTM model results in having the highest accuracy of 88% among the selected algorithms for the sentiment analysis of Twitter data with respect to the selected dataset.