Top Research Papers on Sentiment Analysis

Experimental evaluation of the dataset and classification results of Sentimentor, a tool for sentiment analysis of Twitter data that utilises the naive Bayes Classifier to classify Tweets into positive, negative or objective sets.

The literary approach to computational analysis taken in this article demonstrates the ability of sentiment analysis to link large-scale observations about text data to small-scale features of individual texts and reveals that the sentimental novel itself already constitutes an analytical tradition.

This research has solved the problem of sentiment detection from the noun with a possible solution for duel sentimental words with existing technology and developed a model to do this.

A new method to create a sentiment lexicon for the Arabic language using semi-supervised learning on the WordNet and matching them with an Arabic database is presented.

A novel solution to SA of short informal texts with a main focus on Twitter posts known as "tweets" is introduced, and it is illustrated that the hybrid method outperforms the state-of-the-art unigram baseline.

An overview of sentiment analysis, sentiment classification, methods used for sentiment classification is given.

This work develops a number of neural networks with tailoring loss functions, and applies sentiment embeddings to word-level sentiment analysis, sentence level sentiment classification, and building sentiment lexicons, showing results that consistently outperform context-basedembeddings on several benchmark datasets of these tasks.

This study illustrates how sentiment analysis can be utilised to obtain insights on the experiences of informal L2 learners when they engage with chatbots for L2 learning and suggests informal L1 learners have mixed sentiments on their experiences.

Sentiment analysis or opinion mining is the computational study of people’s opinions, appraisals, and emotions toward entities, events and their attributes in order to gather consumer opinions about its products and those of its competitors.

A common sentiment analysis task, the classification of IMDb (Internet Movie Database) movie reviews, is used to illustrate the methods on a common task that appears frequently in the literature.

This work focuses on Twitter sentiment analysis, employing natural language processing (NLP) techniques to categorize tweets as positive, negative, or neutral, and collects a large dataset of tweets, pre-process the text, and train machine learning models to predict sentiment.

The field of sentiment analysis and opinion mining was introduced and the current state of the art was surveyed and the widely studied topic sentiment analysis based on Twitter and need of opinion spam detection was discussed.

This paper will analyze various classifiers that can be used to detection text based emotions during the Chennai floods and build a prototype to analyses this.

The role of sentiment analysis in the field of education, specifically in understanding and analysing student behaviour based on sentiment data, is explored to provide actionable insights into student engagement, well-being, and academic performance.

Audiovisual input is taken and emotions from video and audio in parallel from audiovisually input are extracted and the overall review is classified as positive, negative or neutral based on the emotions detected.

By theText sentiment analysis method and the text sentiment analysis system, sentiment judgment of multiple entity objects in the text can be realized, accuracy in sentiment analysis is improved, and high-precision automatic sentimentAnalysis is realized.

This work reports a work on automatically merging SenticNet and WordNet-Affect by assigning emotion labels to more than 2700 concepts, which is a major step towards merging these two resources.

This paper has presented a novel framework for using the Concept-level sentiment analysis approach which classifies text based on their semantics rather than syntactic features and provided a lexicon dataset of around 69 k unique concepts.

The prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results are discussed.

This comprehensive introduction to sentiment analysis takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions.

This paper presents an effective method for sentiment analysis of Twitter profiles using deep learning methods, especially Convolutional Neural Networks (CNN), Long-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM).

A novel personalized approach for the sentiment analysis task, based on the intuition that the same sentiment words can carry different sentiment weights for different users, is proposed.

This paper concentrates on sharing in the first place an introduction to Sentiment Analysis; Understanding the need of utilizing sentiment analysis and why it requires a huge amount of consideration.

A new Multimodal Representation Learning (MRL) method for Multimodal Sentiment Analysis (MSA), which facilitates the adaptive interaction between modalities through Cooperative Sentiment Agents, named Co-SA, which excels at discovering diverse cross-modal features.

This comprehensive introduction to sentiment analysis takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions.

A novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text is proposed, which is fully unsupervised.

Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topi...

The prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results are discussed.

This comprehensive introduction to sentiment analysis takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions.

This work proposes two strategies for defining sentence difficulty, which are binary and considers sentences as difficult when the classifiers are unable to correctly assign the sentiment polarity, and uses a six-level difficulty scale.

This work is developing a novel query interface where users can use natural language expressions to help author visual queries and address the knowledge gap.

This comprehensive introduction to sentiment analysis takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions.

Sentiment analysis is a very helpful analysis since it allows us to ascertain the general consensus regarding the sale of goods or forecast stock prices for a certain firm.

Brazilian Consumers’ Sentiments are analyzes in a specific domain using a system, SentiMeter-Br, a Portuguese dictionary focused on a specific domain, and a Twitter and Facebook search framework that extracts online tweets and Facebook posts.

This article analyzes Brazilian Consumers' Sentiments in a specific domain using a system, SentiMeter-Br, in which tenses and negative words are treated in a different way to measure the polarity, the strength of positive or negative sentiment, in short texts extracted from Twitter.

It is demonstrated that the way in which natural language reveals intended sentiment differs across the authors' datasets of Dutch and English texts, and it is suggested that language-specific sentiment scores can separate universal classes of intended sentiment from one another to a limited extent.

A comparative analysis of different approaches for multilingual sentiment analysis is presented, divided into two parts using classification of text without language translation and second using the translation of testing data to a target language, such as English, before classification.

A dynamic service composition mechanism for sentiment analysis based on the social information service classification is proposed and a new quality model to assess the quality of social information services is proposed.

A novel data expansion technique is proposed by creating a sentiment-reversed review for each training and test review, and a dual prediction algorithm is proposed to classify the test reviews by considering two sides of one review.

RNN-Capsule is capable of outputting words with sentiment tendencies reflecting capsules' attributes reflecting the domain specificity of the dataset, and achieves state-of-the-art performance on sentiment classification.

The important part to gather the information is always seems as what the people think. Users express their views and opinions regarding products and services. These opinions are subjective information which represents user’s sentiments, feelings or appraisal related to the same.Every day, millions of people travel around the globe for business, vacations, sightseeing, or other reasons. An astronomical amount of money is spent on tickets, accommodations, food, transportation, and entertainment. Tourism is an information-based business where there are two types of information flow. One flow of i...

This work proposes a model based on the mid-level features of the images that combines the techniques of SentiBank, RCNN (Regions with CNN) and SentiStrength, and shows that this approach achieves better sentiment classification accuracy.

This survey would cover various approaches and methodology used in Sentiment Analysis and Opinion Mining in general, with a focus on Internet text like, Product review, tweets and other social media.

A range of emerging perspectives on sentiment analysis are mapped out and it is argued that these sometimes-competing views need to be brought together, so that analyses of new socio-technical phenomena like sentiment analysis can be rich and rounded.

This work distinguishes three different possible views on newspaper articles ― author, reader and text, which have to be addressed differently at the time of analysing sentiment, and presents work on mining opinions about entities in English language news.

The various difficulties encountered and the most recent developments that can be made to obtain a more precise analysis of the consumer’s feelings and behavior are examined.

The system, Faculty Evaluation System (FES), identifies strengths and weaknesses of teachers on all aspects which are important to students and may be used by higher authorities of the institute in identifying suitable faculty members for different academic and administrative activities of the university.

The goal of this study is to create a system that can evaluate a video and anticipate a person's expression by identifying the various facial expressions by using techniques of CNN and Deep Learning.

The results showed that neutral sentiment was the highest result with a percentage of 48% followed by negative sentiment of 23%, positive sentiment 22%, high positive sentiment 4%, and high negative sentiment 3%.

This paper introduces a word embedding (Word2Vec) technique obtained by unsupervised learning built on large twitter corpora, this process uses co-occurrence statistical characteristics between words in tweets and hidden contextual semantic interrelation.