Hybrid Sentiment Classification of Reviews Using Synonym Lexicon and Word embedding

Sentiment analysis is used in extract some useful information from the given set of documents by using Natural Language Processing (NLP) techniques. These techniques have wide scope in various fields which are dealing with huge amount of data link e-commerce, business and market analysis, social media and review impact of products and movies. Sentiment analysis can be applied over these data for finding the polarity of the data like positive, neutral or negative automatically or many complex sentiments like happiness, sad, anger, joy, etc. for a particular product and services based on user reviews. Sentiment analysis not only able to find the polarity of the reviews. Sentiment analysis utilizes machine learning algorithms with vectorization techniques based on textual documents to train the classifier models. These models are later used to perform sentiment analysis on the given dataset of particular domain on which the classifier model is trained. Vectorization is done for text document by using word embedding based and hybrid vectorization. The proposed methodology focus on fast and accurate sentiment prediction with higher confidence value over the dataset in both Tamil and English

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

Comprehensive Review of Opinion Summarization

The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe

Using sentiment analysis to predict Amazon ratings : a comparative study using dictionaries approaches

This dissertation delves into the domain of sentiment analysis, a computational approach to detect and extract human sentiments from textual data. With the ever-increasing growth of online textual content, especially in the form of reviews, the need to accurately determine customer sentiment has never been more imperative. To explore the efficacy of lexicon-based sentiment analysis models, this study implements 9 models: VADER, TextBlob, NRC Lexicon, SentiWordNet, Pattern, AFINN, Opinion Lexicon, LabMT, and ANEW. These models are tested on an Amazon reviews dataset, which is uniquely accompanied by a rating system in which the accuracy of the sentiment extraction can be assessed. The study then further delves into a comparative analysis, collecting the performance of these models to discern their strengths, weaknesses, and overall utility.Esta dissertação aborda o tema de Sentiment Analysis, uma técnica que permite detetar e extrair sentimentos humanos a partir de texto. Com o crescimento exponencial de dados sob a forma de texto online, particularmente nas avaliações dos consumidores, a necessidade de determinar com precisão os sentimentos destes nunca foi tão imperativo. Esta técnica é essencial para converter os dados textuais em informação que pode ser efetivamente utilizada. Para explorar a eficácia dos modelos de Sentiment Analysis na categoria de abordagem por Dicionário, este estudo implementa nove modelos: VADER, TextBlob, NRC Lexicon, SentiWordNet, Pattern, AFINN, Opinion Lexicon, LabMT e ANEW. Estes modelos são testados numa base de dados que contém avaliações da Amazon e classificações através das quais a precisão da extração de sentimento pode ser avaliada. O estudo aprofunda-se numa análise comparativa, avaliando o desempenho destes modelos para identificar os seus pontos fortes, fracos e a sua utilidade

Extraction of opinionated profiles from comments on web news

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

On the Statistical and Temporal Dynamics of Sentiment Analysis

Despite the broad interest and use of sentiment analysis nowadays, most of the conclusions in current literature are driven by simple statistical representations of sentiment scores. On that basis, the generated sentiment evaluation consists nowadays of encoding and aggregating emotional information from a number of individuals and their populational trends. We hypothesized that the stochastic processes aimed to be measured by sentiment analysis systems will exhibit nontrivial statistical and temporal properties. We established an experimental setup consisting of analyzing the short text messages (tweets) of 6 user groups with different nature (universities, politics, musicians, communication media, technological companies, and financial companies), including in each group ten high-intensity users in their regular generation of traffic on social networks. Statistical descriptors were checked to converge at about 2000 messages for each user, for which messages from the last two weeks were compiled using a custom-made tool. The messages were subsequently processed for sentiment scoring in terms of different lexicons currently available and widely used. Not only the temporal dynamics of the resulting score time series per user was scrutinized, but also its statistical description as given by the score histogram, the temporal autocorrelation, the entropy, and the mutual information. Our results showed that the actual dynamic range of lexicons is in general moderate, and hence not much resolution is given within their end-of-scales. We found that seasonal patterns were more present in the time evolution of the number of tweets, but to a much lesser extent in the sentiment intensity. Additionally, we found that the presence of retweets added negligible effects over standard statistical modes, while it hindered informational and temporal patterns. The innovative Compounded Aggregated Positivity Index developed in this work proved to be characteristic for industries and at ..