Field Of Sentiment Analysis Research Articles

A Novel Approach for Extraction of Distinguishing Emotions for Semantic Granularity Level Sentiment Analysis in Multilingual Context

Introduction: Extraction of distinguishing semantic level emotions posed in multi-languages over social media is an essential task in the field of sentiment analysis or opinion mining. The extraction of emotions expressed in Dravidian or local languages combining with multi-languages over social media has become an essential challenge in the field of big data sentiment analysis. Methods: In the proposed approach, an innovative framework to recognize the sentiments of users in multi-languages or Dravidian languages text data using scientific linguistic theories has been defined. The proposed method used machine learning techniques such as naïve Bayes, support vector machine for fine-grained classification of multilingual text with help of lexicon-based features groups. Results: The results obtained by the experiments conducted on collected benchmark datasets in the proposed approach are outperformed and better in comparison with corpus-based and world level, phrase-level sentiment analysis for multilanguages text. Conclusion: Machine learning technnique SVM has outperformed for sentiment and emotion extraction.

Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

In the field of sentiment analysis, extracting aspects or opinion targets from user reviews about a product is a key task. Extracting the polarity of an opinion is much more useful if we also know the targeted Aspect or Feature. Rule based approaches, like dependency-based rules, are quite popular and effective for this purpose. However, they are heavily dependent on the authenticity of the employed parts-of-speech (POS) tagger and dependency parser. Another popular rule based approach is to use sequential rules, wherein the rules formulated by learning from the user’s behavior. However, in general, the sequential rule-based approaches have poor generalization capability. Moreover, existing approaches mostly consider an aspect as a noun or noun phrase, so these approaches are unable to extract verb aspects. In this article, we have proposed a multi-layered rule-based (ML-RB) technique using the syntactic dependency parser based rules along with some selective sequential rules in separate layers to extract noun aspects. Additionally, after rigorous analysis, we have also constructed rules for the extraction of verb aspects. These verb rules primarily based on the association between verb and opinion words. The proposed multi-layer technique compensates for the weaknesses of individual layers and yields improved results on two publicly available customer review datasets. The F1 score for both the datasets are 0.90 and 0.88, respectively, which are better than existing approaches. These improved results can be attributed to the application of sequential/syntactic rules in a layered manner as well as the capability to extract both noun and verb aspects.

Social listening through sentiment analysis of Twitter data: a case study of Paytm IPO

Purpose. Microblogging sites are being used by people across the globe to share their opinions and to express sentiments for everything in real time. Through social listening, companies analyse the sentiments to assess the way forward, and the researchers use it to analyse the trend or an event and give forward-looking recommendations. The objective of the paper is to analyse the sentiments of people relating to Paytm IPO which can be used to predict the way forward. Design/methodology/approach. The study attempts sentiment analysis. For this purpose, QSR NVIVO 12, the qualitative analysis tool was used to retrieve the tweets from the Twitter website. NCapture was installed for this purpose. Post data cleaning, stemming, query augmentation and classification, the Twitter data was analysed. Findings. The sentiments around the IPO of Paytm have been negative and sarcastic. The extremely negative tweets were near twice the number of extremely positive tweets. Practical implication. The study can help an investor in evaluating the investment that they might be planning in the given company. For the company, whose IPO is being considered, an analysis of the sentiments around the IPO can help in taking corrective measures, if the sentiment is negative, towards reputation building. Originality/value. The study is an original contribution to the extant literature in the field of sentiment analysis.

On Adapting the DIET Architecture and the Rasa Conversational Toolkit for the Sentiment Analysis Task

The Rasa open-source toolkit provides a valuable Natural Language Understanding (NLU) infrastructure to assist the development of conversational agents. In this paper, we show that this infrastructure can seamlessly and effectively be used for other different NLU-related text classification tasks, such as sentiment analysis. The approach is evaluated on three widely used datasets containing movie reviews, namely IMDb, Movie Review (MR) and the Stanford Sentiment Treebank (SST2). The results are consistent across the three databases, and show that even simple configurations of the NLU pipeline lead to accuracy rates that are comparable to those obtained with other state-of-the-art architectures. The best results were obtained when the Dual Intent and Entity Transformer (DIET) architecture was fed with pre-trained word embeddings, surpassing other recent proposals in the sentiment analysis field. In particular, accuracy rates of 0.907, 0.816 and 0.858 were obtained for the IMDb, MR and SST2 datasets, respectively.

Data Mining and Revealing Hidden Sentiment in Tweets Using Spark

Data science is important and scientific value in our lives because it is multi-fields, it's the science that uses scientific methods, processes, algorithms, and systems for the purpose of extracting knowledge and ideas from data whether this data is organized or not. Data science is called 21st century oil to highlight its importance and scientific value in our lives. We paid great attention in this research paper, where we achieved three main steps in the field of data analysis, collecting data from different sources in the Internet and then storing the data within the system, the second step cleaning the data in order to obtain structured data and then applying the algorithms that are responsible for classifying the data. In pursuit of development, we have collected more than 1600000 tweets about the educational process and the possibility of future online education. We give great attention to this field of Sentiment Analysis (SA) and the use of modern Spark technology, which has achieved great success since its emergence. We have succeeded in using Spark in getting a good result, handling data quickly and accurately, which encouraged us to test it on two algorithms of Machine Learning, Support Vector Machine (SVM) & Maximum Entropy (Max Ent).

A survey of deep learning techniques in the field of sentiment analysis for the hindi language

In the domain of natural language processing, sentiment analysis is an important field for any review. Nowadays Indian languages are more popular for any product review. In North India, Hindi is the most widely used language. People having Hindi as their mother tongue can easily express their opinions and thoughts through that language. In the field of research, Hindi language has many challenges due to very less research work, limited data for analysis and less size of corpus data. Deep learning techniques are currently used for predicting feelings. Recurrent Neural Network (RNN), particularly the Long Short Term Memory (LSTM) and Convolution Neural Network (CNN), are two generally used deep learning approaches. Depending on the domain area of application, the strategies are utilized in combinations or as stand-alone procedures. This review paper emphases on the numerous flavours of deep learning approaches employed in various applications of sentiment analysis at the sentence and aspect levels.

A Review of Urdu Sentiment Analysis with Multilingual Perspective: A Case of Urdu and Roman Urdu Language

Research efforts in the field of sentiment analysis have exponentially increased in the last few years due to its applicability in areas such as online product purchasing, marketing, and reputation management. Social media and online shopping sites have become a rich source of user-generated data. Manufacturing, sales, and marketing organizations are progressively turning their eyes to this source to get worldwide feedback on their activities and products. Millions of sentences in Urdu and Roman Urdu are posted daily on social sites, such as Facebook, Instagram, Snapchat, and Twitter. Disregarding people’s opinions in Urdu and Roman Urdu and considering only resource-rich English language leads to the vital loss of this vast amount of data. Our research focused on collecting research papers related to Urdu and Roman Urdu language and analyzing them in terms of preprocessing, feature extraction, and classification techniques. This paper contains a comprehensive study of research conducted on Roman Urdu and Urdu text for a product review. This study is divided into categories, such as collection of relevant corpora, data preprocessing, feature extraction, classification platforms and approaches, limitations, and future work. The comparison was made based on evaluating different research factors, such as corpus, lexicon, and opinions. Each reviewed paper was evaluated according to some provided benchmarks and categorized accordingly. Based on results obtained and the comparisons made, we suggested some helpful steps in a future study.

Fine-Grained Implicit Sentiment in Financial News: Uncovering Hidden Bulls and Bears

The field of sentiment analysis is currently dominated by the detection of attitudes in lexically explicit texts such as user reviews and social media posts. In objective text genres such as economic news, indirect expressions of sentiment are common. Here, a positive or negative attitude toward an entity must be inferred from connotational or real-world knowledge. To capture all expressions of subjectivity, a need exists for fine-grained resources and approaches for implicit sentiment analysis. We present the SENTiVENT corpus of English business news that contains token-level annotations for target spans, polar spans, and implicit polarity (positive, negative, or neutral investor sentiment, respectively). We both directly annotate polar expressions and induce them from existing schema-based event annotations to obtain event-implied implicit sentiment tuples. This results in a large dataset of 12,400 sentiment–target tuples in 288 fully annotated articles. We validate the created resource with an inter-annotator agreement study and a series of coarse- to fine-grained supervised deep-representation-learning experiments. Agreement scores show that our annotations are of substantial quality. The coarse-grained experiments involve classifying the positive, negative, and neutral polarity of known polar expressions and, in clause-based experiments, the detection of positive, negative, neutral, and no-polarity clauses. The gold coarse-grained experiments obtain decent performance (76% accuracy and 63% macro-F1) and clause-based detection shows decreased performance (65% accuracy and 57% macro-F1) with the confusion of neutral and no-polarity. The coarse-grained results demonstrate the feasibility of implicit polarity classification as operationalized in our dataset. In the fine-grained experiments, we apply the grid tagging scheme unified model for <polar span, target span, polarity> triplet extraction, which obtains state-of-the-art performance on explicit sentiment in user reviews. We observe a drop in performance on our implicit sentiment corpus compared to the explicit benchmark (22% vs. 76% F1). We find that the current models for explicit sentiment are not directly portable to our implicit task: the larger lexical variety within implicit opinion expressions causes lexical data scarcity. We identify common errors and discuss several recommendations for implicit fine-grained sentiment analysis. Data and source code are available.

A Review on Negation Role in Twitter Sentiment Analysis

Negation is an important linguistic phenomenon that needs to be considered for identifying correct sentiments from the opinionated data available in digital form. It has the power to alter the polarity or strength of the polarity of affected words. In this paper, the authors present a survey on the negation role that has been done until now in sentiment analysis, specifically Twitter sentiment analysis. The authors discuss the various approaches of modelling negation in Twitter sentiment analysis. In particular, their focus is on negation scope detection and negation handling methods. This article also presents some of the challenges and limits of negation accounting in the field of Twitter sentiment analysis.

A STUDY ON THE SENTIMENT ANALYSIS (POSITIVE, NEGATIVE) OF WORDS APPEARING IN KYRGYZ NEWS BY APPLYING THE DEEP LEARNING-BASED NLP (NATURAL LANGUAGE PROCESSING) TECHNIQUES FOR STUDENTS PRACTICE

This study is theoretical on the sentiment analysis field of deep learning-based natural language processing, which is the world's advanced technology, namely data collection and preprocessing stage, tokenizing stage, Sentiment Dictionary construction stage, positive and negative word extraction stage through sentiment analysis, deep learning introduces major contents and related technologies such as model configuration, execution stage, and data visualization stage. In addition, speech processing technology performed in the data collection stage, STT (Speech to Text) and TTS (Text to Speech) technology will be introduced. In this study, a program was written using various open sources (libraries) including ’keras’ 2.0 version used in the deep learning natural language processing field of the python language base. This study is executed to help students who are studying deep learning natural language processing.

WHAT IS IN A TWEET? DESIGNING A SEMANTIC LEXICON BASED ON EaD STUDENTS' OPINIONS

Among the various challenges regarding distance education is the necessity of reducing the student dropout rate. In this sense, the present research aimed to contribute to the design of a lexical database focused on emotions and opinions that can be incorporated into a predictive evasion software. For the database design, we used the Scup tool to collect 150 tweets containing distance education students’ opinions and analyzed them in the light of Martin and White’s Appraisal Framework, along with five resources related to the sentiment Analysis field, which were taken from Liu’s work. In addition, we used the Aulete dictionary to describe the lexical units found in our corpus to better fit them into the analysis categories. Results showed 220 opinion tokens, which were identified and labeled according to their polarity. Moreover, these tokens were included in the domains attitude (judgment and appreciation) and graduation (sharp and strong) from the linguistic framework used. The results also indicated the necessity of another resource to help identify the use of figurative language, slangs, and extralinguistic elements, such as GIFS and emojis.

Sentiment Analysis for Thai Language in Hotel Domain Using Machine Learning Algorithms

Sentiment analysis is one of the most frequently used aspects of Natural Language Processing (NLP), which utilizes the polarity classification of reviews expressed at the aspect, sentence or document level. Several businesses and organizations utilize this technique to improve production, as well as employee and service efficiency. However, the users’ reviews in our study were expressed in an unstructured data form, which contained spelling errors, leading to complex classifications for both the users and the machine. To solve the problem, a supervised technique of Machine Learning (ML) algorithms can be applied to the data extraction, where classification polarity can be categorized into a positive, negative or neutral class. In this research, we compared nine ML algorithms to determine the most suitable ML algorithm for creating sentiment polarity classification of customer reviews in Thai, which is a low-resource language. The dataset was collected manually from two online agencies (Agoda.com and Booking.com) utilizing a special Thai language. We employed 11 preprocessing steps to clean and handle the large amount of noise data. Next, the Delta TF-IDF, TF-IDF, N-Gram, and Word2Vec techniques were applied to convert the text reviews into vectors, processed with different ML algorithms, to determine sentiment polarity classification and to make accurate comparisons. All ML algorithms were evaluated for sentiment polarity classification with ten-fold cross-validation, with which to compare the values of recall, precision, F1-score and accuracy. The experiment results show that the Support Vector Machine (SVM) using the Delta TF-IDF technique was the best ML algorithm for polarity classification of hotel reviews in the Thai language with the highest accuracy of 89.96%. The results of this research can be applied as the tool for small and medium-sized enterprises within the field of sentiment analysis of the Thai language in the hotel domain.

A new hierarchy framework for feature engineering through multi‐objective evolutionary algorithm in text classification

SummarySentiment classification is a field of sentiment analysis concerned with analyzing opinions, emotions, evaluations, and attitudes regarding a special topic like a product, an organization, a person, or an incident. With the growth of user‐generated content on the Web, this field gained great importance in online reviews. With a wide range of reviews, customers cannot read all reviews. Considering the increasing rate of electronic documents and the urgent need manually mine for keywords that are hard and time‐consuming, doing the same automatically is of high demand. A new framework proposed here to mine and classify users' comments based on mining keywords by applying the sequence pattern mining through the Separation‐Power concept, a multi‐objective evolutionary algorithm based on decomposition with four objectives, and a neural network as the final classifier. Some modifications are made on multi‐objective evolutionary algorithm based on decomposition and Apriori algorithms to improve the text classification efficiency. To evaluate the proposed framework, three datasets applied; which compared with the two methods to measure accuracy, precision, recall, and error‐index. The results indicate that this framework provides a better outcome than its counterparts with 99.45 precision, 99.34 accuracy, 99.48 recall, and 99.28% f‐measure.

A Review on Negation Role in Twitter Sentiment Analysis

Negation is an important linguistic phenomenon that needs to be considered for identifying correct sentiments from the opinionated data available in digital form. It has the power to alter the polarity or strength of the polarity of affected words. In this paper, the authors present a survey on the negation role that has been done until now in sentiment analysis, specifically Twitter sentiment analysis. The authors discuss the various approaches of modelling negation in Twitter sentiment analysis. In particular, their focus is on negation scope detection and negation handling methods. This article also presents some of the challenges and limits of negation accounting in the field of Twitter sentiment analysis.

Multimodal Video Sentiment Analysis Using Deep Learning Approaches, a Survey

Deep learning has emerged as a powerful machine learning technique to employ in multimodal sentiment analysis tasks. In the recent years, many deep learning models and various algorithms have been proposed in the field of multimodal sentiment analysis which urges the need to have survey papers that summarize the recent research trends and directions. This survey paper tackles a comprehensive overview of the latest updates in this field. We present a sophisticated categorization of thirty-five state-of-the-art models, which have recently been proposed in video sentiment analysis field, into eight categories based on the architecture used in each model. The effectiveness and efficiency of these models have been evaluated on the most two widely used datasets in the field, CMU-MOSI and CMU-MOSEI. After carrying out an intensive analysis of the results, we eventually conclude that the most powerful architecture in multimodal sentiment analysis task is the Multi-Modal Multi-Utterance based architecture, which exploits both the information from all modalities and the contextual information from the neighbouring utterances in a video in order to classify the target utterance. This architecture mainly consists of two modules whose order may vary from one model to another. The first module is the Context Extraction Module that is used to model the contextual relationship among the neighbouring utterances in the video and highlight which of the relevant contextual utterances are more important to predict the sentiment of the target one. In most recent models, this module is usually a bidirectional recurrent neural network based module. The second module is an Attention-Based Module that is responsible for fusing the three modalities (text, audio and video) and prioritizing only the important ones. Furthermore, this paper provides a brief summary of the most popular approaches that have been used to extract features from multimodal videos in addition to a comparative analysis between the most popular benchmark datasets in the field. We expect that these findings can help newcomers to have a panoramic view of the entire field and get quick experience from the provided helpful insights. This will guide them easily to the development of more effective models.

Financial Context News Sentiment Analysis for the Lithuanian Language

Financial area analysis is not limited to enterprise performance analysis. It is worth analyzing as wide an area as possible to obtain the full impression of a specific enterprise. News website content is a datum source that expresses the public’s opinion on enterprise operations, status, etc. Therefore, it is worth analyzing the news portal article text. Sentiment analysis in English texts and financial area texts exist, and are accurate, the complexity of Lithuanian language is mostly concentrated on sentiment analysis of comment texts, and does not provide high accuracy. Therefore in this paper, the supervised machine learning model was implemented to assign sentiment analysis on financial context news, gathered from Lithuanian language websites. The analysis was made using three commonly used classification algorithms in the field of sentiment analysis. The hyperparameters optimization using the grid search was performed to discover the best parameters of each classifier. All experimental investigations were made using the newly collected datasets from four Lithuanian news websites. The results of the applied machine learning algorithms show that the highest accuracy is obtained using a non-balanced dataset, via the multinomial Naive Bayes algorithm (71.1%). The other algorithm accuracies were slightly lower: a long short-term memory (71%), and a support vector machine (70.4%).

Sentiment Analysis on Social Media Reviews Datasets with Deep Learning Approach

Thanks to social media, people are now able to leave guiding comments quickly about their favorite restaurants, movies, etc. This has paved the way for the field of sentiment analysis, which brings together various disciplines. In this study, Yelp restaurant reviews and IMDB movie reviews dataset were used together with the data collected from Twitter. Word2Vec (W2V), Global Vector (GloVe) and Bidirectional Encoder Representation (BERT) word embedding methods, Term Frequency-Reverse Document Frequency (TF-IDF), and the Bag-of-Words (BOW) were used on these datasets. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Support Vector Machine (SVM), and Naive Bayes (NB) were used in the sentiment analysis models. Accuracy, F-measure (F), Sensitivity (Sens), Precision (Pre), and Receiver Operating Characteristics (ROC) were used in the evaluation of the model performance. The Accuracy rates of the models created by the Machine Learning (ML) and Deep Learning (DL) methods using the IMDB dataset were in the range of 81%-90% and 84%-94%, respectively. These rates were in the range of 80%-86% and 81%-89% for the Yelp dataset, and in the range of 75%-79% and 85%-98% for the Twitter dataset. The models that incorporated the BERT word embedding method have the best performance, compared to the other models with ML and DL. Therefore, BERT method is recommended for this type of analysis in future studies.

Dual channel sentiment classification model based on grammar rules and multi attention

As convolutional neural networks (CNN) and recurrent neural networks (RNN) have achieved excellent results in the field of Chinese text sentiment analysis. More and more researchers are extracting features of text based on the advantages of CNN and RNN in extracting features. However, the current scholars fail to make full use of sentiment language resources such as sentiment words, negatives and degree adverbs when they adopt deep learning methods. For implicit texts without explicit sentiment words, they cannot fully identify the differences between words and sentiment tendency. At the same time, it fails to consider the grammatical structure of the text, which leads to poor classification effect for some turning sentences or summary sentences. In addition, most of the models are mainly input in the form of word vector. For English, it is very convenient to segment words through spaces between words, but for Chinese, there may be inaccurate word segmentation, which will reduce the accuracy of classification. To solve these problems, a dual channel sentiment classification model based on grammar rules and multi attention (DCGA) is proposed. Firstly, the text with clear sentiment tendency is obtained according to the grammar rules, and the local features of the text are extracted by CNN channel. Considering that the grammar rules may ignore the context information, bi-directional long short term memory network (Bi-LSTM) channel is used to extract the global features containing the context information, and attention pooling is used to improve the sentiment information extracted by CNN channel. Then, the part of speech attention mechanism is used to mine the implicit sentiment features in the sentiment text to solve the poor classification effect caused by the existence of implicit text. Finally, the local features obtained by CNN channel and the global features obtained by RNN channel are fused for classification. Experimental results on four Chinese comment text datasets show that the proposed model outperforms most existing methods in accuracy.

AraSenCorpus: A Semi-Supervised Approach for Sentiment Annotation of a Large Arabic Text Corpus

At a time when research in the field of sentiment analysis tends to study advanced topics in languages, such as English, other languages such as Arabic still suffer from basic problems and challenges, most notably the availability of large corpora. Furthermore, manual annotation is time-consuming and difficult when the corpus is too large. This paper presents a semi-supervised self-learning technique, to extend an Arabic sentiment annotated corpus with unlabeled data, named AraSenCorpus. We use a neural network to train a set of models on a manually labeled dataset containing 15,000 tweets. We used these models to extend the corpus to a large Arabic sentiment corpus called “AraSenCorpus”. AraSenCorpus contains 4.5 million tweets and covers both modern standard Arabic and some of the Arabic dialects. The long-short term memory (LSTM) deep learning classifier is used to train and test the final corpus. We evaluate our proposed framework on two external benchmark datasets to ensure the improvement of the Arabic sentiment classification. The experimental results show that our corpus outperforms the existing state-of-the-art systems.

Systematic reviews in sentiment analysis: a tertiary study

With advanced digitalisation, we can observe a massive increase of user-generated content on the web that provides opinions of people on different subjects. Sentiment analysis is the computational study of analysing people's feelings and opinions for an entity. The field of sentiment analysis has been the topic of extensive research in the past decades. In this paper, we present the results of a tertiary study, which aims to investigate the current state of the research in this field by synthesizing the results of published secondary studies (i.e., systematic literature review and systematic mapping study) on sentiment analysis. This tertiary study follows the guidelines of systematic literature reviews (SLR) and covers only secondary studies. The outcome of this tertiary study provides a comprehensive overview of the key topics and the different approaches for a variety of tasks in sentiment analysis. Different features, algorithms, and datasets used in sentiment analysis models are mapped. Challenges and open problems are identified that can help to identify points that require research efforts in sentiment analysis. In addition to the tertiary study, we also identified recent 112 deep learning-based sentiment analysis papers and categorized them based on the applied deep learning algorithms. According to this analysis, LSTM and CNN algorithms are the most used deep learning algorithms for sentiment analysis.