Features For Sentiment Analysis Research Articles

Sentiment Analysis and Deep Learning Based Cyber Bullying Detection in Twitter Dataset

When somebody, usually a teenager, abuses or harasses individual on the internet and other digital places, mainly on social networking platforms, this is termed as cyberbullying. Cyberbullying, like all types of bullying, produces psychological, emotional, and physical distress. Every individual's reaction to being bullied is diverse, but research has discovered certain common patterns. In a recent study, we introduced a technique called Hybrid Firefly Artificial Neural Networks (HFANN) to combat cyberbullying. Nevertheless, without considering the sentiment analysis features, accuracy of cyber bullying identification is lowered in this study. The Sentiment Analysis and Deep Learning based Cyber Bullying Detection (SADL-CDD) approach is used in the suggested research approach to address this issue. The punctuations, urls, html tags, and emoticons from the input tweet comments are removed first in this study project. Sentiment feature extraction is performed after pre-processing to improve classification accuracy. The Modified Fruit Fly Algorithm (MFFA) is used to choose the best features from the extracted features. Following feature selection, cyber bullying detection is carried out using a Hybrid Recurrent Residual Convolutional Neural Network (HRecRCNN). The experimental outcome of this study indicates the efficiency of the suggested approach. In comparison to current algorithms, the SADL-CDD method delivers improved classification performance with respect to reduced time complexity, greater precision, recall, f-measure, and accuracy.

Video sentiment analysis with bimodal information-augmented multi-head attention

Humans express feelings or emotions via different channels. Take language as an example, it entails different sentiments under different visual–acoustic contexts. To precisely understand human intentions as well as reduce the misunderstandings caused by ambiguity and sarcasm, we should consider multimodal signals including textual, visual and acoustic signals. The crucial challenge is to fuse different modalities of features for sentiment analysis. To effectively fuse the information carried by different modalities and better predict the sentiments, we design a novel multi-head attention based fusion network, which is inspired by the observations that the interactions between any two pair-wise modalities are different and they do not equally contribute to the final sentiment prediction. By assigning the acoustic–visual, acoustic–textual and visual–textual features with reasonable attention and exploiting a residual structure, we attend to attain the significant features. We conduct extensive experiments on four public multimodal datasets including one in Chinese and three in English. The results show that our approach outperforms the existing methods and can explain the contributions of bimodal interaction in multiple modalities.

Fusing Audio, Textual, and Visual Features for Sentiment Analysis of News Videos

This paper presents a novel approach to perform sentiment analysis of news videos, based on the fusion of audio, textual and visual clues extracted from their contents. The proposed approach aims at contributing to the semiodiscoursive study regarding the construction of the ethos (identity) of this media universe, which has become a central part of the modern-day lives of millions of people. To achieve this goal, we apply state-of-the-art computational methods for (1) automatic emotion recognition from facial expressions, (2) extraction of modulations in the participants' speeches and (3) sentiment analysis from the closed caption associated to the videos of interest. More specifically, we compute features, such as, visual intensities of recognized emotions, field sizes of participants, voicing probability, sound loudness, speech fundamental frequencies and the sentiment scores (polarities) from text sentences in the closed caption. Experimental results with a dataset containing 520 annotated news videos from three Brazilian and one American popular TV newscasts show that our approach achieves an accuracy of up to 84% in the sentiments (tension levels) classification task, thus demonstrating its high potential to be used by media analysts in several applications, especially, in the journalistic domain.

Aspect Based Online Sentiment Analysis Product Review and Feature Using Machine Learning

Today people, exchanging their thoughts through online web forums, blogs, and different platforms for social media. In online shopping, they are giving reviews and opinions on other various products, brands, and services. Their thoughts towards a product are do not only purchase decisions of the consumers but also improves the product quality about their requirements and find out the product's particular problem and get an excellent solution on that product. The present system concentrate on the peer-reviewed review model (User-generated review) and global qualification i.e., rating and, tries to classify the semantic aspect and emotions at the time aspect level from the data to investigate general sense feel of the reviews. SJASM represents each review document in the format of opinion pairs and, along with simulating the terms of appearance and the corresponding opinion words of the study, consideration for the hidden aspect and the sentiment detection. The current system is designed as a recommendation system Physiological Language Processing (NLP) Technique to read reviews and using Naïve Baye's Classification automatically. We have also extracted the thoughts of the product characteristics. Here admin can analyze the opinion pair that actually what is defect in the finished product so in future the market of that product will increase. This system to extract product aspects and corresponding opinions from consumer ratings on the internet. Different machine learning algorithms are discussed in Naïve Bayes is considered in order to classify of sentiments, and variables such as precision, recall, F-score, and accuracy are used to assess a classifier's performance.

Detection of Online Fake Reviews Based on Text Emotion

online groups offer clients with ways to overpower a few data hurdles and limitations, like the challenge to get self-governing data about movies and for the co-occurrence of positive and negative reactions within reviews. False reviews will disturb such choices because of misleading data, causing commercial disadvantages for the customers. Recognition of false opinions has thus expected very large concentration now days. But, many websites have only concentrated on handling the problematical comments and reviews. Our work tends to categorize people opinions into groups of true orfalse polarization by employing text feature analysis. In our work, our team analyze people opinions by implementing Sentiment Analysis techniques to recognize false opinions. Sentiment Analysis and text feature categorization techniques were used to a database containing people opinions. Furthermore, the estimation reviews acquired from reviewers could be categorized into good or bad opinions, that could be utilized by a customer to choose a movie. Further the proposed technique will graph based on the classification of true and fake reviews as the analysis of good and bad reviews for a product (movie). It will help us to predict the ratio of fake reviews to true reviews easily. To estimate the accomplishment of SA technique, this work has employed accurateness, exactness, recollection and F-degree as a performance rate.

Text2Plot: Sentiment Analysis by Creating 2D Plot Representations of Texts

AbstractWe introduce a novel approach for generating 2D RGB color images with a plot from the micro text (tweet) to be used for the overall polarity classification process of sentiment analysis. Researchers generally use word embedding and external resource‐based embedding techniques for text preprocessing of sentiment analysis through machine learning, neural networks, and natural language processing approaches. We sought to identify alternative ways to represent tweets for text classification. According to the experimental results, using the new ‘Text2Plot’ representation method could increase F1 scores by 27.2% for Convolutional neural networks (CNNs), 10.3% for support vector machine, and 4.4% for random forest models compared to using simple vectors as features for sentiment analysis. Hence, we propose this new method as a useful text representation approach for sentiment analysis, natural language processing tasks, and image processing problems. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

TensSent: a tensor based sentimental word embedding method

The representation of words as vectors, conventionally known as word embeddings, has drawn considerable attention in recent years as feature learning techniques for natural language processing. The majority of these methods operate solely on the semantic and the syntactic aspects of a text, remaining oblivious to sentimental information. However, as numerous words with opposite polarities may appear in similar contexts, such as “interesting” and “boring”, the exclusive use of context-oriented information lacks the required particulars for generating word embeddings as features in sentiment analysis. Along this thread, the present study proposes two novel unsupervised models to integrating word polarity information and word co-occurrences as more tailored features for sentiment analysis. Word polarity and co-occurrence come together in the form of a tensor and tensor factorization is employed for generating the word embeddings. The experimental results on IMDB and SemEval-task2 datasets demonstrate the relatively higher performance of the proposed method compared to baseline approaches on the tasks of document-level sentiment analysis by 4.

Some Features of Sentiment Analysis for Russian Language Posts and Comments from Social Networks

Sentiment analysis of different language texts is one of the very popular machine learning tasks. The complexity of its solution depends both on the characteristics of a particular language, and on the length of the evaluated texts. In our work, we consider the task of creating a sentiment analysis software tool for Russian posts and comments from the most popular social networks without any domain restriction. The features of constructing both the algorithmic and the software parts of the problem are described, some quality and performance metrics of the suggested neural network system are presented.

ASPECT-BASED SENTIMENT ANALYSIS OF POLITICAL DISCOURSE

The existing systems for accurate sentiment analysis are mainly based on statistical and mathematical principles. However, more promising are the works that are devoted to the study of the linguistic features of the evaluation expression. The results of this formalization can be applied both in the field of affective computing for further improvement of automatic systems and for linguistics and related sciences. The novelty of this study lies mainly in the development of an algorithm based on the identified linguistic rules. In addition, the research material is political discourse, which has not yet been studied enough by specialists of affective computing. The relevance of this work is justified by the growing need for categorization of information published on the Internet. The purpose of the study is to develop a system for machine sentiment analysis of English-language political texts, as well as to identify aspects and their distribution for subsequent use in enhancement. The article discusses the linguistic features of sentiment analysis and suggests a classification of linguistic units with sentiment potential in relation to levels of language structure. The results of an experiment on testing the operation of the sentiment analysis system, conducted on 300 news articles and user comments taken from reddit.com/r/politics, are also presented. The accuracy of the system is 92%. In addition, the selected 40 comments were manually marked up and tagged; during this process the expert identified 25 aspects. Furthermore, 3 formal patterns were identified in the distribution of aspect terms, which is necessary for creating an automatic system. The first peculiarity is that the aspect terms are repeated in two consecutive sentences. The second is that aspect terms are often the themes of sentences. Finally, the third — a high frequency of distribution of aspect terms at the beginning and end of the text (document) was revealed.

Word2Sent: A new learning sentiment‐embedding model with low dimension for sentence level sentiment classification

AbstractWord embedding models become an increasingly important method that embeds words into a high dimensional space. These models have been widely utilized to extract semantic and syntactic features for sentiment analysis. However, using word embedding models cannot be sufficient for sentiment analysis tasks because they do not contain sentiment features. Therefore, word embedding models do not adequately meet the comprehensive needs of sentiment analysis applications that rely on recognizing the polarity of a sentence. In this paper, we propose a sentiment embedding model (Word2Sent model) to tackle the weaknesses of the existing word embedding models for sentiment analysis applications. We developed this model based on the Continuous Bag‐of‐Words model and SentiWordNet lexicon to learn sentiment embedding for each word from its surrounding context words. It preserves semantic and syntactic features and captures implicitly sentiment ones. Besides, it can predict sentiment features in a very low sentiment embeddings dimension than traditional ones. The proposed method provides an improved sentiment classification performance and lowers the computational complexity. Both the accuracy performance and processing time results obtained indicate that the proposed model is particularly promising.

Hybrid convolutional bidirectional recurrent neural network based sentiment analysis on movie reviews

AbstractSentiment analysis is the process of extracting the opinions of customers from online reviews. In general, customers express their reviews in natural language. It becomes a complex task when applying sentiment analysis on those reviews. In earlier stages, word‐level features with various feature weighting methods such as Bag of Words, TF‐IDF, and Word2Vec were applied for sentiment analysis and deep learning networks are not explored much. We considered phrase level and sentence level features instead of applying word‐level features for sentiment analysis and also enhanced by applying various deep learning techniques. In this article, we have proposed a hybrid convolutional bidirectional recurrent neural network model (CBRNN) by combining two‐layer convolutional neural network (CNN) with a bidirectional gated recurrent unit (BGRU). In the proposed CBRNN model, the CNN layer extracts the rich set of phrase‐level features and BGRU captures the chronological features through long term dependency in a multi‐layered sentence. The proposed approach was evaluated on two benchmark datasets and compared with various baselines. The experimental results show that the proposed hybrid model provides better results than any other models with an F1 score of 87.62% and 77.4% on IMDB and Polarity datasets,respectively. Our CBRNN model outperforms the state of the art by 2%‐4% on these two datasets. It is also observed that, the time taken for training is slightly higher than the existing approaches with the substantial improvement in the performance.

On the evaluation and combination of state-of-the-art features in Twitter sentiment analysis

Sentiment analysis of short informal texts, such as tweets, remains a challenging task due to their particular characteristics. Much effort has been made in the literature of Twitter sentiment analysis to achieve an effective and efficient representation of tweets. In this context, distinct types of features have been proposed and employed, from the simple n-gram representation to meta-features to word embeddings. Hence, in this work, using a relevant set of twenty-two datasets of tweets, we present a thorough evaluation of features by means of different supervised learning algorithms. We evaluate not only a rich set of meta-features examined in state-of-the-art studies, but also a significant collection of pre-trained word embedding models. Also, we evaluate and analyze the effect of combining those distinct types of features in order to detect which combination may provide core information in the polarity detection task in Twitter sentiment analysis. For this purpose, we exploit different strategies for combination, such as feature concatenation and ensemble learning techniques, and show that the sentiment detection of tweets benefits from combining different types of features proposed in the literature.

Quantum-inspired multimodal fusion for video sentiment analysis

We tackle the crucial challenge of fusing different modalities of features for multimodal sentiment analysis. Mainly based on neural networks, existing approaches largely model multimodal interactions in an implicit and hard-to-understand manner. We address this limitation with inspirations from quantum theory, which contains principled methods for modeling complicated interactions and correlations. In our quantum-inspired framework, the word interaction within a single modality and the interaction across modalities are formulated with superposition and entanglement respectively at different stages. The complex-valued neural network implementation of the framework achieves comparable results to state-of-the-art systems on two benchmarking video sentiment analysis datasets. In the meantime, we produce the unimodal and bimodal sentiment directly from the model to interpret the entangled decision.

RETRACTED ARTICLE: Topic flexible aspect based sentiment analysis using minimum spanning tree with Cuckoo search

A study by which the opinions, emotions, evaluations, appraisals, and sentiments of people towards different entities is expressed in the form of a text is called Sentiment Analysis (SA). The primary task of a sentiment analysis which is based on the aspects is the extraction of various aspects of the entities and the determination of the sentiments that correspond to the terms of aspects which are commented in the review document. Recently, there is a huge rise in interest to make an identification of various sentiments and aspects at the same time. Feature selection in terms of aspects of entity plays a crucial role in deciding the efficiency of the sentiment analysis; hence the Minimum Spanning Tree (MST) is used for feature selection.The MST has certain major advantages such as being computable quickly. The selection of optimal features to aid in better accuracy of classification is done through MST optimized with Cuckoo search algorithm. The features in sentiment analysis are classified using Random Forest (RF) and Ada Boost classifiers.The Random Forest (RF) is probably the most accurate among all algorithms of learning available today. The Ada Boost algorithm has a performance that is extremely good owing to its ability to be able to generate the expanding diversity. This was done in order to bring about an improvement in the final ensemble, as it contained several weak classifiers.

Attention-Based Modality-Gated Networks for Image-Text Sentiment Analysis

Sentiment analysis of social multimedia data has attracted extensive research interest and has been applied to many tasks, such as election prediction and products evaluation. Sentiment analysis of one modality (e.g., text or image) has been broadly studied. However, not much attention has been paid to the sentiment analysis of multimodal data. Different modalities usually have information that is complementary. Thus, it is necessary to learn the overall sentiment by combining the visual content with text description. In this article, we propose a novel method—Attention-Based Modality-Gated Networks (AMGN)—to exploit the correlation between the modalities of images and texts and extract the discriminative features for multimodal sentiment analysis. Specifically, a visual-semantic attention model is proposed to learn attended visual features for each word. To effectively combine the sentiment information on the two modalities of image and text, a modality-gated LSTM is proposed to learn the multimodal features by adaptively selecting the modality that presents stronger sentiment information. Then a semantic self-attention model is proposed to automatically focus on the discriminative features for sentiment classification. Extensive experiments have been conducted on both manually annotated and machine weakly labeled datasets. The results demonstrate the superiority of our approach through comparison with state-of-the-art models.

Adaptive Feature Generation for Speech Emotion Recognition

The issue of emotion recognition has received considerable critical attention in artificial intelligence and machine learning. In sentiment analysis fields, researchers recognize emotional states from speech, electroencephalograms, and images, etc. The speech signal is among the most widely used in emotion recognition. There are many speech features, including pitch, energy, linear prediction coefficients, mel-frequency cepstral coefficients, and the Teager energy operator. In this study, we explore the critical speech features for sentiment analysis. We modify our previous feature-generation method, which applies low-variance filtering and principal component analysis (for grouped features) to identify the features. We do not utilize between-class scatter here, but rather, the between class–scatter and within class–scatter ratio. Grouping is achieved according to the number of features—not correlation values. For an objective evaluation, we use the Ryerson Audio-Visual Database of Emotional Speech and Song, with a performance evaluation conducted in terms of classifier accuracy and computational complexity. Finally, we propose an effective feature-generation method to find the critical features for emotion recognition from speech.

Emoji-Based Sentiment Analysis Using Attention Networks

Emojis are frequently used to express moods, emotions, and feelings in social media. There has been much research on emojis and sentiments. However, existing methods mainly face two limitations. First, they treat emojis as binary indicator features and rely on handcrafted features for emoji-based sentiment analysis. Second, they consider the sentiment of emojis and texts separately, not fully exploring the impact of emojis on the sentiment polarity of texts. In this article, we investigate a sentiment analysis model based on bidirectional long short-term memory, and the model has two advantages compared with the existing work. First, it does not need feature engineering. Second, it utilizes the attention approach to model the impact of emojis on text. An evaluation on 10,042 manually labeled Sina Weibo showed that our model achieves much better performance compared with several strong baselines. To facilitate the related research, our corpus will be publicly available at https://github.com/yx100/emoji.

Sentiment Analysis: A Contrastive Study of its Techniques

The current research focuses on one of the techniques of Natural Language Processing (NLP) known as Sentiment Analysis. NLP provides the computer with an ability to understand, process and interpret natural human language. Nowadays, there are many data generated through the web and its platforms which is unstructured according to many surveys. Using NLP, we could convert this unstructured data into a structured format. Among them, one of the critical tasks of this sentiment analysis is to produce a quick summary of opinions based on the product's features which helps many manufacturers know how their products are doing in the market and what necessary things they should make to improve their products. This paper highlights the critical features of Sentiment Analysis, Data Sources, techniques involved and addressing its importance in our present world by analyzing it's application areas.

Cross-Domain Recommendation Based on Sentiment Analysis and Latent Feature Mapping

Cross-domain recommendation is a promising solution in recommendation systems by using relatively rich information from the source domain to improve the recommendation accuracy of the target domain. Most of the existing methods consider the rating information of users in different domains, the label information of users and items and the review information of users on items. However, they do not effectively use the latent sentiment information to find the accurate mapping of latent features in reviews between domains. User reviews usually include user’s subjective views, which can reflect the user’s preferences and sentiment tendencies to various attributes of the items. Therefore, in order to solve the cold-start problem in the recommendation process, this paper proposes a cross-domain recommendation algorithm (CDR-SAFM) based on sentiment analysis and latent feature mapping by combining the sentiment information implicit in user reviews in different domains. Different from previous sentiment research, this paper divides sentiment into three categories based on three-way decision ideas—namely, positive, negative and neutral—by conducting sentiment analysis on user review information. Furthermore, the Latent Dirichlet Allocation (LDA) is used to model the user’s semantic orientation to generate the latent sentiment review features. Moreover, the Multilayer Perceptron (MLP) is used to obtain the cross domain non-linear mapping function to transfer the user’s sentiment review features. Finally, this paper proves the effectiveness of the proposed CDR-SAFM framework by comparing it with existing recommendation algorithms in a cross-domain scenario on the Amazon dataset.

Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis

Multimodal language analysis often considers relationships between features based on text and those based on acoustical and visual properties. Text features typically outperform non-text features in sentiment analysis or emotion recognition tasks in part because the text features are derived from advanced language models or word embeddings trained on massive data sources while audio and video features are human-engineered and comparatively underdeveloped. Given that the text, audio, and video are describing the same utterance in different ways, we hypothesize that the multimodal sentiment analysis and emotion recognition can be improved by learning (hidden) correlations between features extracted from the outer product of text and audio (we call this text-based audio) and analogous text-based video. This paper proposes a novel model, the Interaction Canonical Correlation Network (ICCN), to learn such multimodal embeddings. ICCN learns correlations between all three modes via deep canonical correlation analysis (DCCA) and the proposed embeddings are then tested on several benchmark datasets and against other state-of-the-art multimodal embedding algorithms. Empirical results and ablation studies confirm the effectiveness of ICCN in capturing useful information from all three views.