Showing papers on "Sentiment analysis published in 2023"

Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research

Abstract: Sentiment analysis as a field has come a long way since it was first introduced as a task nearly 20 years ago. It has widespread commercial applications in various domains like marketing, risk management, market research, and politics, to name a few. Given its saturation in specific subtasks — such as sentiment polarity classification — and datasets, there is an underlying perception that this field has reached its maturity. In this article, we discuss this perception by pointing out the shortcomings and under-explored, yet key aspects of this field necessary to attain true sentiment understanding. We analyze the significant leaps responsible for its current relevance. Further, we attempt to chart a possible course for this field that covers many overlooked and unanswered questions.

Sentiment analysis for product review

Abstract: Now a day’s internet is most valuable source of learning, getting idea, reviews for a product. Sentiment analysis is a type of data mining that measures the user’s opinions through natural language processing(NLP). Sentiment analysis is also called as a opinion mining. It uses a data mining processes and techniques to extract and capture data for analysis the subjective opinion of a document or collection of documents like reviews, social media, e-commerce sites. In the field of sentiment analysis there are many algorithms have to tackle NLP problems to identify the positive and negative reviews of the user’s for your products on online market. Data used in this, we are study online product review collected from Amazon.com, Redif.com, Flipkart.com.

The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements

Abstract: Truly real-life data presents a strong, but exciting challenge for sentiment and emotion research. The high variety of possible ‘in-the-wild’ properties makes large datasets such as these indispensable with respect to building robust machine learning models. A sufficient quantity of data covering a deep variety in the challenges of each modality to force the exploratory analysis of the interplay of all modalities has not yet been made available in this context. In this contribution, we present MuSe-CaR, a first of its kind multimodal dataset. The data is publicly available as it recently served as the testing bed for the 1st Multimodal Sentiment Analysis Challenge, and focused on the tasks of emotion, emotion-target engagement, and trustworthiness recognition by means of comprehensively integrating the audio-visual and language modalities. Furthermore, we give a thorough overview of the dataset in terms of collection and annotation, including annotation tiers not used in this year's MuSe 2020. In addition, for one of the sub-challenges -- predicting the level of trustworthiness -- no participant outperformed the baseline model, and so we propose a simple, but highly efficient Multi-Head-Attention network that exceeds using multimodal fusion the baseline by around 0.2 CCC (almost 50 % improvement).

Sentiment Analysis of Text Reviews Using Lexicon-Enhanced Bert Embedding (LeBERT) Model with Convolutional Neural Network

Abstract: Sentiment analysis has become an important area of research in natural language processing. This technique has a wide range of applications, such as comprehending user preferences in ecommerce feedback portals, politics, and in governance. However, accurate sentiment analysis requires robust text representation techniques that can convert words into precise vectors that represent the input text. There are two categories of text representation techniques: lexicon-based techniques and machine learning-based techniques. From research, both techniques have limitations. For instance, pre-trained word embeddings, such as Word2Vec, Glove, and bidirectional encoder representations from transformers (BERT), generate vectors by considering word distances, similarities, and occurrences ignoring other aspects such as word sentiment orientation. Aiming at such limitations, this paper presents a sentiment classification model (named LeBERT) combining sentiment lexicon, N-grams, BERT, and CNN. In the model, sentiment lexicon, N-grams, and BERT are used to vectorize words selected from a section of the input text. CNN is used as the deep neural network classifier for feature mapping and giving the output sentiment class. The proposed model is evaluated on three public datasets, namely, Amazon products’ reviews, Imbd movies’ reviews, and Yelp restaurants’ reviews datasets. Accuracy, precision, and F-measure are used as the model performance metrics. The experimental results indicate that the proposed LeBERT model outperforms the existing state-of-the-art models, with a F-measure score of 88.73% in binary sentiment classification.

A BERT Framework to Sentiment Analysis of Tweets

Abstract: Sentiment analysis has been widely used in microblogging sites such as Twitter in recent decades, where millions of users express their opinions and thoughts because of its short and simple manner of expression. Several studies reveal the state of sentiment which does not express sentiment based on the user context because of different lengths and ambiguous emotional information. Hence, this study proposes text classification with the use of bidirectional encoder representations from transformers (BERT) for natural language processing with other variants. The experimental findings demonstrate that the combination of BERT with CNN, BERT with RNN, and BERT with BiLSTM performs well in terms of accuracy rate, precision rate, recall rate, and F1-score compared to when it was used with Word2vec and when it was used with no variant.

Arabic Tweets-Based Sentiment Analysis to Investigate the Impact of COVID-19 in KSA: A Deep Learning Approach

Abstract: The World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) a pandemic on 11 March 2020. The evolution of this pandemic has raised global health concerns, making people worry about how to protect themselves and their families. This has greatly impacted people’s sentiments. There was a dire need to investigate a large amount of social data such as tweets and others that emerged during the post-pandemic era for the assessment of people’s sentiments. As a result, this study aims at Arabic tweet-based sentiment analysis considering the COVID-19 pandemic in Saudi Arabia. The datasets have been collected in two different periods in three major regions in Saudi Arabia, which are: Riyadh, Dammam, and Jeddah. Tweets were annotated with three sentiments: positive, negative, and neutral after due pre-processing. Convolutional neural networks (CNN) and bi-directional long short memory (BiLSTM) deep learning algorithms were applied for classifying the sentiment of Arabic tweets. This experiment showed that the performance of CNN achieved 92.80% accuracy. The performance of BiLSTM was scored at 91.99% in terms of accuracy. Moreover, as an outcome of this study, an overwhelming upsurge in negative sentiments were observed in the dataset during COVID-19 compared to the negative sentiments of the dataset before COVID-19. The technique has been compared with the state-of-the-art techniques in the literature and it was observed that the proposed technique is promising in terms of various performance parameters.

Gated recurrent unit with multilingual universal sentence encoder for Arabic aspect-based sentiment analysis

Abstract: The increasing interactive content in the Internet motivated researchers and data scientists to conduct Aspect-Based Sentiment Analysis (ABSA) research to understand the various sentiments and the different aspects of a product in a single user’s comment. Determining the various aspects along with their polarities (positive, negative, or neutral) from a single comment is a challenging problem. To this end, we have designed and developed a deep learning model based on Gated Recurrent Units (GRU) and features extracted using the Multilingual Universal Sentence Encoder (MUSE). The proposed Pooled-GRU model trained on a Hotels’ Arabic reviews to address two ABSA tasks: (1) aspect extraction, and (2) aspect polarity classification. The proposed model achieved high results with 93.0% F1 score in the former task and 90.86% F1 score in the latter task. Our experimental results show that our proposed model outperforms the baseline model and the related research methods evaluated on the same dataset. More precisely, our proposed model showed 62.1% improvement in the F1 score over the baseline model for the aspect extraction task and 15% improvement in the accuracy over the baseline model for the aspect polarity classification task.

Multi-Label Sentiment Analysis on 100 Languages With Dynamic Weighting for Label Imbalance

Abstract: We investigate cross-lingual sentiment analysis, which has attracted significant attention due to its applications in various areas including market research, politics and social sciences. In particular, we introduce a sentiment analysis framework in multi-label setting as it obeys Plutchik wheel of emotions. We introduce a novel dynamic weighting method that balances the contribution from each class during training, unlike previous static weighting methods that assign non-changing weights based on their class frequency. Moreover, we adapt the focal loss that favors harder instances from single-label object recognition literature to our multi-label setting. Furthermore, we derive a method to choose optimal class-specific thresholds that maximize the macro-f1 score in linear time complexity. Through an extensive set of experiments, we show that our method obtains the state-of-the-art performance in 7 of 9 metrics in 3 different languages using a single model compared to the common baselines and the best-performing methods in the SemEval competition. We publicly share our code for our model, which can perform sentiment analysis in 100 languages, to facilitate further research.

Research on non-dependent aspect-level sentiment analysis

Abstract: As a popular research field of sentiment analysis, aspect-based sentiment analysis focuses on the emotional expression in different aspects. However, the current research is not precise enough in dividing the aspects of sentiment analysis. The problem of semantic overlap between aspects occurs. Furthermore, in many cases, one aspect may be contained in several sub-aspects. When the study only focused on the emotion tends in one or several sub-aspects, the results of sentiment analysis may be distorted. To deal with these problems, we propose the concept of non-dependent aspects by analyzing the dependencies among aspects and a method for dividing non-dependent aspects. Through theoretical analysis, we demonstrate that our proposed sentiment analysis results based on non-dependent aspects are more accurate than the original one, and non-dependent aspects can be easily transferred to a new corpus. The experiments on real-world data are also supporting the results of theoretical analysis. The range of accuracy of non-dependent aspects is improved by 1.9%–13.4% than before.

Embedding Refinement Framework for Targeted Aspect-Based Sentiment Analysis

Abstract: The state-of-the-art approaches to targeted aspect-based sentiment analysis (TABSA) are mostly built on deep neural networks with attention mechanisms. One problem is that embeddings of targets and aspects are either pre-trained from large external corpora or randomly initialized. We argue that affective commonsense knowledge and words indicative of sentiment could be used to learn better target and aspect embeddings. We therefore propose an embedding refinement framework called RAEC ( R efining A ffective E mbedding from C ontext), in which sentiment concepts extracted from affective commonsense knowledge and word relative location information are incorporated to derive context-affective embeddings. Furthermore, a sparse coefficient vector is exploited in refining the embeddings of targets and aspects separately. In this way, embeddings of targets and aspects can capture the highly relevant affective words. Experimental results on two benchmark datasets show that our framework can be easily integrated with existing embedding-based TABSA models and achieves state-of-the-art results compared to models relying on pre-trained word embeddings or built on other embedding refinement methods.

A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis

Abstract: There is an exponential growth in textual content generation every day in today's world. In-app messaging such as Telegram and WhatsApp, social media websites such as Instagram and Facebook, e-commerce websites like Amazon, Google searches, news publishing websites, and a variety of additional sources are the possible suppliers. Every instant, all these sources produce massive amounts of text data. The interpretation of such data can help business owners analyze the social outlook of their product, brand, or service and take necessary steps. The development of a consumer review summarization model using Natural Language Processing (NLP) techniques and Long short-term memory (LSTM) to present summarized data and help businesses obtain substantial insights into their consumers' behavior and choices is the topic of this research. A hybrid approach for analyzing sentiments is presented in this paper. The process comprises pre-processing, feature extraction, and sentiment classification. Using NLP techniques, the pre-processing stage eliminates the undesirable data from input text reviews. For extracting the features effectively, a hybrid method comprising review-related features and aspect-related features has been introduced for constructing the distinctive hybrid feature vector corresponding to each review. The sentiment classification is performed using the deep learning classifier LSTM. We experimentally evaluated the proposed model using three different research datasets. The model achieves the average precision, average recall, and average F1-score of 94.46%, 91.63%, and 92.81%, respectively.

Survey on sentiment analysis: evolution of research methods and topics

Abstract: Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword co-occurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates keyword co-occurrence analysis with a community detection algorithm. This survey not only compares and analyzes the connections between research methods and topics over the past two decades but also uncovers the hotspots and trends over time, thus providing guidance for researchers. Furthermore, this paper presents broad practical insights into the methods and topics of sentiment analysis, while also identifying technical directions, limitations, and future work.

Back to common sense: Oxford dictionary descriptive knowledge augmentation for aspect-based sentiment analysis

Abstract: Aspect-based Sentiment Analysis (ABSA) is a crucial natural language understanding (NLU) research field which aims to accurately recognize reviewers’ opinions on different aspects of products and services. Despite the prominence of recent ABSA applications, mainstream ABSA approaches inevitably rely on large-scale supervised corpora, and their final performances is susceptible to the quality of the training datasets. However, annotating sufficient data is labour intensive, which presents a significant barrier for generalizing a high-quality sentiment analysis model. Nonetheless, humans can make more accurate judgement based on their external background knowledge, such as factoid triples knowledge and event causality. Inspired by the investigations on external knowledge enhancement strategies in other popular NLP research, we propose a novel knowledge augmentation framework for ABSA, named the Oxford Dictionary descriptive knowledge-infused aspect-based sentiment analysis (DictABSA). Comprehensive experiments with many state-of-the-art approaches on several widely used benchmarks demonstrate that our proposed DictABSA significantly outperforms previous mainstream ABSA methods. For instance, compared with the baselines, our BERT-based knowledge infusion strategy achieves a substantial 6.42% and 5.26% absolute accuracy gain when adopting BERT-SPC on SemEval2014 and ABSA-DeBERTa on ACLShortData, respectively. Furthermore, to effectively make use of dictionary knowledge we devise several alternative knowledge infusion strategies. Extensive experiments using different knowledge infused strategies further demonstrate that the proposed knowledge infusion strategies effectively enhance the sentiment polarity identification capability. The Python implementation of our DictABSA is publicly available at https://github.com/albert-jin/DictionaryFused-E2E-ABSA.

Beyond Sentiment Analysis: A Review of Recent Trends in Text Based Sentiment Analysis and Emotion Detection

Abstract: Sentiment Analysis is probably one of the best-known area in text mining. However, in recent years, as big data rose in popularity more areas of text classification are being explored. Perhaps the next task to catch on is emotion detection, the task of identifying emotions. This is because emotions are the finer grained information which could be extracted from opinions. So besides writer sentiments, writer emotion is also a valuable data. Emotion detection can be done using text, facial expressions, verbal communications and brain waves; however, the focus of this review is on text-based sentiment analysis and emotion detection. The internet has provided an avenue for the public to express their opinions easily. These expressions not only contain positive or negative sentiments, it contains emotions as well. These emotions can help in social behaviour analysis, decision and policy makings for companies and the country. Emotion detection can further support other tasks such as opinion mining and early depression detection. This review provides a comprehensive analysis of the shift in recent trends from text sentiment analysis to emotion detection and the challenges in these tasks. We summarize some of the recent works in the last five years and look at the methods they used. We also look at the models of emotion classes that are generally referenced. The trend of text-based emotion detection has shifted from the early keyword-based comparisons to machine learning and deep learning algorithms that provide more flexibility to the task and better performance.

News-based intelligent prediction of financial markets using text mining and machine learning: A systematic literature review

Abstract: Researchers and practitioners have attempted to predict the financial market by analyzing textual (e.g., news articles and social media) and numeric data (e.g., hourly stock prices, and moving averages). Among textual data, while many papers have been published that analyze social media, news content has gained limited attention in predicting the stock market. Acknowledging that news is critical in predicting the stock market, the focus of this systematic review is on papers investigating machine learning and text mining techniques to predict the stock market using news. Using Kitchenham’s methodology, we present a systematic review of the literature on intelligent financial market prediction, examining data mining and machine learning approaches and the employed datasets. From five digital libraries, we identified 61 studies from 2015–2022 for synthesis and interpretation. We present notable gaps and barriers to predicting financial markets, then recommend future research scopes. Various input data, including numerical (stock prices and technical indicators) and textual data (news text and sentiment), have been employed for news-based stock market prediction. News data collection can be costly and time-consuming: most studies have used custom crawlers to gather news articles; however, there are financial news databases available that could significantly facilitate news collection. Furthermore, although most datasets have covered fewer than 100K records, deep learning and more sophisticated artificial neural networks can process enormous datasets faster, improving future model performance. There is a growing trend toward using artificial neural networks, particularly recurrent neural networks and deep learning models, from 2018 to 2021. Furthermore, regression and gradient-boosting models have been developed for stock market prediction during the last four years. Although word embedding approaches for feature representation have been employed recently with good accuracy, emerging language models may be a focus for future research. Advanced natural language processing methods like transformers have undeniably contributed to intelligent stock market prediction. However, stock market prediction has not yet taken full advantage of them.

Gender Bias in BERT - Measuring and Analysing Biases through Sentiment Rating in a Realistic Downstream Classification Task

Abstract: Pretrained language models are publicly available and constantly finetuned for various real-life applications. As they become capable of grasping complex contextual information, harmful biases are likely increasingly intertwined with those models. This paper analyses gender bias in BERT models with two main contributions: First, a novel bias measure is introduced, defining biases as the difference in sentiment valuation of female and male sample versions. Second, we comprehensively analyse BERT?s biases on the example of a realistic IMDB movie classifier. By systematically varying elements of the training pipeline, we can conclude regarding their impact on the final model bias. Seven different public BERT models in nine training conditions, i.e. 63 models in total, are compared. Almost all conditions yield significant gender biases. Results indicate that reflected biases stem from public BERT models rather than task-specific data, emphasising the weight of responsible usage.

TSA-CNN-AOA: Twitter sentiment analysis using CNN optimized via arithmetic optimization algorithm

Abstract: COVID-19, a novel virus from the coronavirus family, broke out in Wuhan city of China and spread all over the world, killing more than 5.5 million people. The speed of spreading is still critical as an infectious disease, and it causes more and more deaths each passing day. COVID-19 pandemic has resulted in many different psychological effects on people’s mental states, such as anxiety, fear, and similar complex feelings. Millions of people worldwide have shared their opinions on COVID-19 on several social media websites, particularly on Twitter. Therefore, it is likely to minimize the negative psychological impact of the disease on society by obtaining individuals’ views on COVID-19 from social media platforms, making deductions from their statements, and identifying negative statements about the disease. In this respect, Twitter sentiment analysis (TSA), a recently popular research topic, is used to perform data analysis on social media platforms such as Twitter and reach certain conclusions. The present study, too, proposes TSA using convolutional neural network optimized via arithmetic optimization algorithm (TSA-CNN-AOA) approach. Firstly, using a designed API, 173,638 tweets about COVID-19 were extracted from Twitter between July 25, 2020, and August 30, 2020 to create a database. Later, significant information was extracted from this database using FastText Skip-gram. The proposed approach benefits from a designed convolutional neural network (CNN) model as a feature extractor. Thanks to arithmetic optimization algorithm (AOA), a feature selection process was also applied to the features obtained from CNN. Later, K-nearest neighbors (KNN), support vector machine, and decision tree were used to classify tweets as positive, negative, and neutral. In order to measure the TSA performance of the proposed method, it was compared with different approaches. The results demonstrated that TSA-CNN-AOA (KNN) achieved the highest tweet classification performance with an accuracy rate of 95.098. It is evident from the experimental studies that the proposed approach displayed a much higher TSA performance compared to other similar approaches in the existing literature.

Artificial Intelligence and Sentiment Analysis: A Review in Competitive Research

Abstract: As part of a business strategy, effective competitive research helps businesses outperform their competitors and attract loyal consumers. To perform competitive research, sentiment analysis may be used to assess interest in certain themes, uncover market conditions, and study competitors. Artificial intelligence (AI) has improved the performance of multiple areas, particularly sentiment analysis. Using AI, sentiment analysis is the process of recognizing emotions expressed in text. AI comprehends the tone of a statement, as opposed to merely recognizing whether particular words within a group of text have a negative or positive connotation. This article reviews papers (2012–2022) that discuss how competitive market research identifies and compares major market measurements that help distinguish the services and goods of the competitors. AI-powered sentiment analysis can be used to learn what the competitors’ customers think of them across all aspects of the businesses.

What Tweets and YouTube comments have in common? Sentiment and graph analysis on data related to US elections 2020

Abstract: Most studies analyzing political traffic on Social Networks focus on a single platform, while campaigns and reactions to political events produce interactions across different social media. Ignoring such cross-platform traffic may lead to analytical errors, missing important interactions across social media that e.g. explain the cause of trending or viral discussions. This work links Twitter and YouTube social networks using cross-postings of video URLs on Twitter to discover the main tendencies and preferences of the electorate, distinguish users and communities’ favouritism towards an ideology or candidate, study the sentiment towards candidates and political events, and measure political homophily. This study shows that Twitter communities correlate with YouTube comment communities: that is, Twitter users belonging to the same community in the Retweet graph tend to post YouTube video links with comments from YouTube users belonging to the same community in the YouTube Comment graph. Specifically, we identify Twitter and YouTube communities, we measure their similarity and differences and show the interactions and the correlation between the largest communities on YouTube and Twitter. To achieve that, we have gather a dataset of approximately 20M tweets and the comments of 29K YouTube videos; we present the volume, the sentiment, and the communities formed in YouTube and Twitter graphs, and publish a representative sample of the dataset, as allowed by the corresponding Twitter policy restrictions.

Natural Language Processing Implementation for Sentiment Analysis on Tweets

Abstract: In this article, we describe our early efforts with sentiment analysis on tweets. This project is meant to extract sentiment from tweets depending on their topic matter. It utilises natural language processing methods to determine the emotion associated with a certain issue. We used three different approaches to identify emotions in our study: classification based on subjectivity, semantic association and classification based on polarity. The experiment makes advantage of emotion lexicons by establishing the grammatical relationship between them and the subject. Due to the unique structure of tweets, the proposed method outperforms current text sentiment analysis methods.

Deep learning-based user experience evaluation in distance learning

Abstract: The Covid-19 pandemic caused uncertainties in many different organizations, institutions gained experience in remote working and showed that high-quality distance education is a crucial component in higher education. The main concern in higher education is the impact of distance education on the quality of learning during such a pandemic. Although this type of education may be considered effective and beneficial at first glance, its effectiveness highly depends on a variety of factors such as the availability of online resources and individuals' financial situations. In this study, the effectiveness of e-learning during the Covid-19 pandemic is evaluated using posted tweets, sentiment analysis, and topic modeling techniques. More than 160,000 tweets, addressing conditions related to the major change in the education system, were gathered from Twitter social network and deep learning-based sentiment analysis models and topic models based on latent dirichlet allocation (LDA) algorithm were developed and analyzed. Long short term memory-based sentiment analysis model using word2vec embedding was used to evaluate the opinions of Twitter users during distance education and also, a topic model using the LDA algorithm was built to identify the discussed topics in Twitter. The conducted experiments demonstrate the proposed model achieved an overall accuracy of 76%. Our findings also reveal that the Covid-19 pandemic has negative effects on individuals 54.5% of tweets were associated with negative emotions whereas this was relatively low on emotion reports in the YouGov survey and gender-rescaled emotion scores on Twitter. In parallel, we discuss the impact of the pandemic on education and how users' emotions altered due to the catastrophic changes allied to the education system based on the proposed machine learning-based models.

Multi-Tier Sentiment Analysis of Social Media Text Using Supervised Machine Learning

Abstract: Sentiment Analysis (SA) is often referred to as opinion mining. It is defined as the extraction, identification, or characterization of the sentiment from text. Generally, the sentiment of a textual document is classified into binary classes i.e., positive and negative. However, fine-grained classification provides a better insight into the sentiments. The downside is that fine-grained classification is more challenging as compared to binary. On the contrary, performance deteriorates significantly in the case of multi-class classification. In this study, pre-processing techniques and machine learning models for the multi-class classification of sentiments were explored. To augment the performance, a multi-layer classification model has been proposed. Owing to similitude with social media text, the movie reviews dataset has been used for the implementation. Supervised machine learning models namely Decision Tree, Support Vector Machine, and Naïve Bayes models have been implemented for the task of sentiment classification. We have compared the models of single-layer architecture with multi-tier model. The results of Multi-tier model have slight improvement over the single-layer architecture. Moreover, multi-tier models have better recall which allow our proposed model to learn more context. We have discussed certain shortcomings of the model that will help researchers to design multi-tier models with more contextual information.