Top Research Papers on Sentiment Analysis

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.

This project focuses on applying sentiment analysis techniques to movie reviews, aiming to develop an efficient model for automatically classifying sentiments as positive, negative, or neutral, to create a robust sentiment analysis of accurately categorizing the sentiment conveyed in movie reviews.

In the ever-expanding realm of the internet, where vast amounts of data are generated daily, platforms like Twitter become hubs for expressing opinions.This survey delves into sentiment analysis on Twitter, focusing on the challenges posed by unstructured, diverse opinions.Leveraging natural language processing (NLP) techniques, a robust sentiment analysis framework is constructed.The framework utilizes machine learning algorithms such as Naive Bayes, Maximum Entropy, and Support Vector Machine (SVM).A diverse dataset is collected, preprocessed, and used to train and evaluate the model.

A weak supervised neural model that aims at learning a set of sentiment clusters embedding from the sentence global representation of the target domain, and an attention-based LSTM model to address aspect-level sentiment analysis task based on the sentiment score retrieved from the proposed dictionary.

Sentiment Knowledge Enhanced Pre-training (SKEP) is introduced in order to learn a unified sentiment representation for multiple sentiment analysis tasks, and significantly outperforms strong pre-training baseline, and achieves new state-of-the-art results on most of the test datasets.

The study puts forth two key insights: relative efficacy of four sentiment analysis algorithms and undisputed superiority of pre-trained advanced supervised deep learning algorithm BERT in sentiment classification from text.

A danmaku sentiment dictionary is constructed and a new method using sentiment dictionary and Naïve Bayes for the sentiment analysis of dan maku reviews is presented, which has a significant effect on sentiment score and polarity detection.

The proposed method relies on a pre-trained language model and can capture the dependence on both targets and aspects for sentiment prediction and achieves a high performance in detecting target-aspect-sentiment triples even for the implicit target cases.

A comprehensive exploration of LLMs’ proficiency in sentiment analysis, a core task in marketing research for understanding consumer emotions, opinions, and perceptions, finds that linguistic features such as the presence of lengthy, content-laden words improve classification performance, while other features such as single-sentence reviews and less structured social media text documents reduce performance.

The extraction of tweets pertaining to the General Elections of India in 2019 is carried out along with the study of sentiments among Twitter users towards the major national political parties participating in the electoral process to prepare the classification model and compare it with the classical machine learning models.

This study proposes text classification with the use of bidirectional encoder representations from transformers (BERT) for natural language processing with other variants to improve accuracy rate, precision rate, recall rate, and F1-score.

This work explores an approach utilizing state-of-the-art machine translation technology and performs sentiment analysis on the English translation of a foreign language text and indicates that entity sentiment scores obtained by the method are statistically significantly correlated across nine languages of news sources and five languages of a parallel corpus.

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 proposed approach based on a feature ensemble model related to tweets containing fuzzy sentiment by taking into account elements such as lexical, word-type, semantic, position, and sentiment polarity of words proves effective in improving the performance of tweet sentiment analysis in terms of the $F_{1}$ score.

The study concludes that while majority of the people throughout the world are taking a positive and hopeful approach, there are instances of fear, sadness and disgust exhibited worldwide.

This article uses SenticNet to extract natural language concepts and fine-tune several feature types on a subset of MuSe-CAR to explore the content of a video as well as learning to predict emotional valence, arousal, and speaker topic classes.

Today, exploiting sentiment analysis has become popular in designing recommender systems in various fields, including the restaurant and food area. However, most of the sentiment analysis-based restaurant recommender systems only use static information such as food quality, price, and service quality. The analysis of users’ opinions and the extraction of their food preferences lead to the provision of personalized recommendations, which is a research gap in literature; In this paper, a context-aware recommender system is proposed that extracts the food preferences of individuals from their com...

In this paper, we investigate the utility of linguistic features for detecting the sentiment of Twitter messages. We evaluate the usefulness of existing lexical resources as well as features that capture information about the informal and creative language used in microblogging. We take a supervied approach to the problem, but leverage existing hashtags in the Twitter data for building training data.

According to this analysis, LSTM and CNN algorithms are the most used deep learning algorithms for sentiment analysis.

This cutting-edge book brings together experts in the field to provide a multidimensional perspective on sentiment analysis, covering both foundational and advanced methodologies. Readers will gain insights into the latest natural language processing and machine learning techniques that power sentiment analysis, enabling the extraction of nuanced emotions from text. Key Features: •State-of-the-Art Techniques: Explore the most recent advancements in sentiment analysis, from deep learning approaches to sentiment lexicons and beyond. •Real-World Applications: Dive into a wide range of application...

This paper proposes to tackle various ABSA tasks in a unified generative framework with two types of paradigms, namely annotation-style and extraction-style modeling, to enable the training process by formulating each ABSA task as a text generation problem.

This work does sentiment analysis on text reviews by using Long Short-Term Memory (LSTM), and recently, thanks to their ability to handle large amounts of knowledge, neural networks have achieved a good success on sentiment classification.

The hybrid models increased the accuracy for sentiment analysis compared with single models on all types of datasets, especially the combination of deep learning models with SVM, and the reliability of the latter was significantly higher.

An overview of the method for completing this task as well as the applications of sentiment analysis is discussed, which evaluates, compares, and investigates the approaches used to gain a comprehensive understanding of their advantages and disadvantages.

Financial sentiment analysis plays a crucial role in decoding market trends and guiding strategic trading decisions. Despite the deployment of advanced deep learning techniques and language models to refine sentiment analysis in finance, this study breaks new ground by investigating the potential of large language models, particularly ChatGPT 3.5, in financial sentiment analysis, with a strong emphasis on the foreign exchange market (forex). Employing a zero-shot prompting approach, we examine multiple ChatGPT prompts on a meticulously curated dataset of forex-related news headlines, measuring...

An in-depth qualitative study of the most important research works in this context by discussing strengths and limitations of existing approaches, and surveys both approaches that leverage machine translation or transfer learning to adapt English resources to Arabic and approaches that stem directly from the Arabic language.

A new sentiment analysis model-SLCABG, which is based on the sentiment lexicon and combines Convolutional Neural Network (CNN) and attention-based Bidirectional Gated Recurrent Unit (BiGRU).

This article analyzes those works that address the relationship between sentiment analysis, opinion mining, and emotion understanding in advertising to clarify the current state of these studies, explore issues, methods, findings, themes, and gaps as well as to define their significance within the current convergence advertising research scenario.

This research analysed the content of online reviews including the text of reviews and their rankings to support opinion mining and found that opinion mining has significantly supported knowledge and decision-making.

In this study, the twitter data has been pulled out from Twitter social media, through python programming language, using Tweepy library, then by using TextBlob library in python, the sentiment analysis operation has been done and a visualized presentation regarding the results and further explanation are provided.

This work addresses the crucial challenge of fusing different modalities of features for multimodal sentiment analysis with inspirations from quantum theory, which contains principled methods for modeling complicated interactions and correlations.

Text mining implementation in seafarers’ medical documents can generate better knowledge of medical issues that often happened onboard to improve quality of healthcare, the possibility of fewer medical errors, and low costs.

An evaluation platform is designed which is used to assess the effectiveness and performance of various sentiment analysis approaches, based on combinations of text representation methods and machine-learning classifiers, and shows improved efficiency of contextual embeddings in sentiment analysis.

A comprehensive comparative study on the most effective approaches used for Arabic sentiment analysis, which re-implement most of the existing approaches and test their effectiveness on three of the most popular benchmark datasets for Arabic SA.

Sentiment is fundamental to human communication. Countless marketing applications mine opinions from social media communication, news articles, customer feedback, or corporate communication. Various sentiment analysis methods are available and new ones have recently been proposed. Lexicons can relate individual words and expressions to sentiment scores. In contrast, machine learning methods are more complex to interpret, but promise higher accuracy, i.e., fewer false classifications. We propose an empirical framework and quantify these trade-offs for different types of research questions, data...

This paper proposes a comprehensive review of the existing methods for sentiment analysis in social networks from an academic perspective, and explores their applications in different contexts such as stock market value, politics, and cyberbullying in educational centers.

A novel model, called emoticon smoothed language model (ESLAM), is presented, which is to train a language model based on the manually labeled data, and then use the noisy emoticon data for smoothing.

The process of capturing data from social media over the years along with the similarity detection based on similar choices of the users in social networks are addressed.

This review paper provides understanding into levels of sentiment analysis, various emotion models, and the process of sentimentAnalysis and emotion detection from text and discusses the challenges faced during sentiment and emotion analysis.

Understanding sentiments and opinions toward vaccination using Twitter may help public health agencies to increase positive messaging and eliminate opposing messages in order to enhance vaccine uptake.