Sentiment Resources Research Articles

Characteristics of opinions in the societal and non-societal domains

With the increasing availability of user opinions on the web, understanding the distinct nature of opinions in societal and non-societal contexts becomes crucial for opinion mining and sentiment analysis tasks. Societal topics, encompassing social unrest, terrorist acts, and government policies, differ significantly from non-societal topics like product reviews, movie reviews, and restaurant reviews. Given the regional specificity of societal issues and the lack of sentiment-annotated resources for them, this paper highlights the need to comprehend the differences in opinions between these domains for effective sentiment analysis. Through statistical text and network analysis, it investigates word usage, sentiment word association, and homogeneity in societal versus non-societal contexts. The study also explores graph-based analysis as a novel approach to sentiment analysis, considering its advantage in easily expanding context through the addition of nodes, as opposed to the complexity of inserting relevant tokens in text. The findings suggest that while non-societal sentiment resources might not be directly applicable to societal domains, graph-based analysis offers promising avenues for sentiment analysis in diverse societal topics.

Short Text Semantic Sentiment Analysis Based on Dual Channel Aspect Attention in Intelligent Systems

Traditional deep learning models for text sentiment analysis fail to fully harness the contextual semantic information of aspect nodes or use prior sentiment resources. This paper proposes a dual channel sentiment analysis model named M2BERT-BLSTM AA that is based on an enhanced Bidirectional Encoder Representations from Transformers(BERT)and Bidirectional Long short-term memory(BLSTM) model and incorporates a Dual Attention Mechanism. Firstly, an emotional resource database is constructed using existing emotional resources. Secondly, vectors are concatenated following mean and max pooling along the dimension of sentence length. These semantic features mitigate evaluation imbalance.Then the text and sentiment information are encoded separately, using distinct Attention Mechanism(Att-M) to extract contextual relationships and emotional features. The model's Aspect-Based multi-class sentiment prediction accuracies on the three Chinese datasets of Meituan ordering, restaurants, and laptops are 75.2%, 87.5%, and 75%, respectively, showing improved performance on sentiment classification.

Twitter Sentiment Geographical Index Dataset

Promoting well-being is one of the key targets of the Sustainable Development Goals at the United Nations. Many national and city governments worldwide are incorporating Subjective Well-Being (SWB) indicators into their agenda, to complement traditional objective development and economic metrics. In this study, we introduce the Twitter Sentiment Geographical Index (TSGI), a location-specific expressed sentiment database with SWB implications, derived through deep-learning-based natural language processing techniques applied to 4.3 billion geotagged tweets worldwide since 2019. Our open-source TSGI database represents the most extensive Twitter sentiment resource to date, encompassing multilingual sentiment measurements across 164 countries at the admin-2 (county/city) level and daily frequency. Based on the TSGI database, we have created a web platform allowing researchers to access the sentiment indices of selected regions in the given time period.

Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges

Sentiment analysis has come long way since it was introduced as a natural language processing task nearly 20 years ago. Sentiment analysis aims to extract the underlying attitudes and opinions toward an entity. It has become a powerful tool used by governments, businesses, medicine, marketing, and others. The traditional sentiment analysis model focuses mainly on text content. However, technological advances have allowed people to express their opinions and feelings through audio, image and video channels. As a result, sentiment analysis is shifting from unimodality to multimodality. Multimodal sentiment analysis brings new opportunities with the rapid increase of sentiment analysis as complementary data streams enable improved and deeper sentiment detection which goes beyond text-based analysis. Audio and video channels are included in multimodal sentiment analysis in terms of broadness. People have been working on different approaches to improve sentiment analysis system performance by employing complex deep neural architectures. Recently, sentiment analysis has achieved significant success using the transformer-based model. This paper presents a comprehensive study of different sentiment analysis approaches, applications, challenges, and resources then concludes that it holds tremendous potential. The primary motivation of this survey is to highlight changing trends in the unimodality to multimodality for solving sentiment analysis tasks.

Count Me Too: Sentiment Analysis of Roman Sindhi Script

Social media has given voice to people around the globe. However, all voices are not counted due to the scarcity of lexical computational resources. Such resources could harness the torrent of social media text data. Computational resources for rich languages such as English are available. More are being developed, meanwhile strengthening and enhancing the current ones. However, Roman Sindhi, a resource-poor writing style, is a phonetically rich language lacking computational resources, creating a working space for researchers. This work attempts to develop lexical sentiment resources that will help calculate the public opinion expressed in Roman Sindhi and bring their point of view into the limelight. This work is one of the initial efforts to develop lexical Roman Sindhi sentiment dictionary resources to help detect sentiment orientation in a text. Furthermore, it also developed two interfaces to leverage the lexical resources—a Roman Sindhi to English translator (RoSET) that translates a Roman Sindhi feature into an equivalent English word and a Roman Sindhi rule-based sentiment scorer ( RBRS3) that assigns sentiment score to a Roman Sindhi script features. The results obtained from the developed system accommodated the bilingual dataset (Roman Sindhi + English) more adequately. An increase of 20.8% was recorded for positive sentence detection, and a 16% increase was obtained for negative sentences, whereas neutral sentences were marginalized to a lower number (59.31% decrease). The resultant system makes those public voices expressed in the Roman Sindhi script get counted, which otherwise are in vain.

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

The key sentimental words in the text cannot be paid attention to effectively, and language knowledge such as the text information and the sentimental resources are relied on. Therefore, it is necessary to make full use of this unique sentimental information to achieve the best performance of the model. In order to solve the problems, a method based on the fusion of the convolutional neural network and the bidirectional GRU network text sentiment analysis capsule model to analyze the ideological and political education of public opinion is put forward. In this model, each sentiment category is combined with the attention mechanism to generate feature vectors to construct sentiment capsules. Finally, the text sentiment categories are judged according to the attributes of the capsules. The model is tested on MR, IMDB, SST-5, and the data set of the ideological and political education review. Experimental results show that compared with MC-CNN-LSTM, the readiness rate of the proposed model is improved by 5.1%, 2.8%, 2.8%, and 1.6% on four public Chinese and English data sets, respectively. Compared with LR-Bi-LSTM, NSCL, and multi-Bi-LSTM models, the accuracy of the proposed MC-BiGRU-Capsule model on MR and SST-5 data are 3.2%, 2.4%, and 3.4% higher than that of the LR-Bi-LSTM, NSCL, and multi-Bi-LSTM models, respectively. It also shows a better classification effect on multiclassification data sets. It is concluded that compared with other baseline models, this method has a better classification effect.

Chinese EmoBank: Building Valence-Arousal Resources for Dimensional Sentiment Analysis

An increasing amount of research has recently focused on dimensional sentiment analysis that represents affective states as continuous numerical values on multiple dimensions, such as valence-arousal (VA) space. Compared to the categorical approach that represents affective states as distinct classes (e.g., positive and negative), the dimensional approach can provide more fine-grained (real-valued) sentiment analysis. However, dimensional sentiment resources with valence-arousal ratings are very rare, especially for the Chinese language. Therefore, this study aims to: (1) Build a Chinese valence-arousal resource called Chinese EmoBank, the first Chinese dimensional sentiment resource featuring various levels of text granularity including 5,512 single words, 2,998 multi-word phrases, 2,582 single sentences, and 2,969 multi-sentence texts. The valence-arousal ratings are annotated by crowdsourcing based on the Self-Assessment Manikin (SAM) rating scale. A corpus cleanup procedure is then performed to improve annotation quality by removing outlier ratings and improper texts. (2) Evaluate the proposed resource using different categories of classifiers such as lexicon-based, regression-based, and neural-network-based methods, and comparing their performance to a similar evaluation of an English dimensional sentiment resource.

Improving Attention Model Based on Cognition Grounded Data for Sentiment Analysis

Attention models are proposed in sentiment analysis and other classification tasks because some words are more important than others to train the attention models. However, most existing methods either use local context based information, affective lexicons, or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. First,a reading prediction model is built using eye-tracking data as dependent data and other features in the context as independent data. The predicted reading time is then used to build a cognition grounded attention layer for neural sentiment analysis. Our model can capture attentions in context both in terms of words at sentence level as well as sentences at document level. Other attention mechanisms can also be incorporated together to capture other aspects of attentions, such as local attention, and affective lexicons. Results of our work include two parts. The first part compares our proposed cognition ground attention model with other state-of-the-art sentiment analysis models. The second part compares our model with an attention model based on other lexicon based sentiment resources. Evaluations show that sentiment analysis using cognition grounded attention model outperforms the state-of-the-art sentiment analysis methods significantly. Comparisons to affective lexicons also indicate that using cognition grounded eye-tracking data has advantages over other sentiment resources by considering both word information and context information. This work brings insight to how cognition grounded data can be integrated into natural language processing (NLP) tasks.

Sentiment Analysis Using Multi-Head Attention Capsules With Multi-Channel CNN and Bidirectional GRU

Existing text sentiment analysis methods mostly rely on a large number of language knowledge and sentiment resources. This paper proposes the Multi-channel convolution and bidirectional GRU multi-head attention capsule (AT-MC-BiGRU-Capsule), which uses vector neurons to replace scalar neurons to model text emotions, and uses capsules to characterize text emotions. In addition, traditional methods cannot extract the multi-level features of text sequence well. Multi-head attention can encode the dependencies between words, capture sentiment words in text, and using Convolutional Neural Network (CNN) and Bidirectional gated recurrent unit network (Bi-GRU) to extract local features and global semantic features of text respectively, the global average pooling layer is introduced to obtain the multi-level feature representation of the text sequence more comprehensively. This paper selects three English datasets and one Chinese dataset in the general corpus of sentiment classification to conduct experiments, and achieves better results than other baseline models.

Industrial Policies, Investor Sentiment and Enterprise Resource Allocation Efficiency

In order to guide capital investment and improve the efficiency of resource allocation, the Chinese government has formulated and promulgated a series of industrial policies. However, China’s capital market has the typical characteristics of “policy-driven market”. Industrial policies often lead to sharp fluctuations in investor sentiment in the stock market, and have a substantial impact on enterprise resource allocation behavior and efficiency. Based on the “inefficient” capital market hypothesis, this paper intends to answer the following questions: Do industrial policies cause the fluctuation of investor sentiment in the stock market? What kind of physical impact does investor sentiment have on enterprise resource allocation behavior and efficiency? What is the underlying mechanism?This paper takes the industries that are not involved in industrial policies as the reference group, and takes the industries that are encouraged to support and restrict as the two groups. The empirical study finds that supportive industrial policies cause the rise of investor sentiment and stimulate enterprise investment, which will aggravate the overinvestment of enterprises and alleviate the underinvestment of enterprises; restrictive industrial policies lead to the decline of investor sentiment and inhibit enterprise investment, which will alleviate the overinvestment of enterprises and aggravate the underinvestment of enterprises. This shows that investor sentiment plays a mediating role in the process of industrial policies guiding resource allocation. Further research shows that the greater the degree of dependence on equity financing or management equity incentive, the more sensitive the enterprise resource allocation is to the fluctuation of investor sentiment, which indicates that the deep mechanism of the influence of investor sentiment on enterprise resource allocation under the exogenous impact of policies lies in that the enterprise has the motivation to rely on equity financing and cater to market sentiment. Finally, on the whole, the impact of investor sentiment on enterprise resource allocation under the exogenous impact of industrial policies results in the damage of corporate value, and there are significant differences in different channels of influence. Capital investment by reducing the cost of equity financing by investor sentiment significantly improves corporate value, while the resource allocation that caters to market sentiment significantly damages corporate value.This paper may have the following three aspects of innovation and contribution: First, it reveals the role of “exogenous factors” of industrial policies in the formation and evolution of investor sentiment, which helps to understand the impact of public policies on the stock market from the perspective of micro enterprises. Second, based on the limited rationality of investors, it studies the role of inefficient stock market in the process of industrial policies guiding enterprise resource allocation, which will help to understand the relationship among government, market and enterprise. Third, the research results have important policy and practical significance: In the process of using industrial policies to guide enterprise resource allocation, the government should not ignore the intermediate link of financial market’s response to industrial policies, and should pay attention to and effectively use the resource allocation function of “ineffective capital market”.

Sentimental classification analysis of polarity multi-view textual data using data mining techniques

The data and information available in most community environments is complex in nature. Sentimental data resources may possibly consist of textual data collected from multiple information sources with different representations and usually handled by different analytical models. These types of data resource characteristics can form multi-view polarity textual data. However, knowledge creation from this type of sentimental textual data requires considerable analytical efforts and capabilities. In particular, data mining practices can provide exceptional results in handling textual data formats. Besides, in the case of the textual data exists as multi-view or unstructured data formats, the hybrid and integrated analysis efforts of text data mining algorithms are vital to get helpful results. The objective of this research is to enhance the knowledge discovery from sentimental multi-view textual data which can be considered as unstructured data format to classify the polarity information documents in the form of two different categories or types of useful information. A proposed framework with integrated data mining algorithms has been discussed in this paper, which is achieved through the application of X-means algorithm for clustering and HotSpot algorithm of association rules. The analysis results have shown improved accuracies of classifying the sentimental multi-view textual data into two categories through the application of the proposed framework on online polarity user-reviews dataset upon a given topics.

Cross Lingual Sentiment Analysis: A Clustering-Based Bee Colony Instance Selection and Target-Based Feature Weighting Approach

The lack of sentiment resources in poor resource languages poses challenges for the sentiment analysis in which machine learning is involved. Cross-lingual and semi-supervised learning approaches have been deployed to represent the most common ways that can overcome this issue. However, performance of the existing methods degrades due to the poor quality of translated resources, data sparseness and more specifically, language divergence. An integrated learning model that uses a semi-supervised and an ensembled model while utilizing the available sentiment resources to tackle language divergence related issues is proposed. Additionally, to reduce the impact of translation errors and handle instance selection problem, we propose a clustering-based bee-colony-sample selection method for the optimal selection of most distinguishing features representing the target data. To evaluate the proposed model, various experiments are conducted employing an English-Arabic cross-lingual data set. Simulations results demonstrate that the proposed model outperforms the baseline approaches in terms of classification performances. Furthermore, the statistical outcomes indicate the advantages of the proposed training data sampling and target-based feature selection to reduce the negative effect of translation errors. These results highlight the fact that the proposed approach achieves a performance that is close to in-language supervised models.

Bidirectional LSTM with self-attention mechanism and multi-channel features for sentiment classification

There are a lot of linguistic knowledge and sentiment resources nowadays, but in the current research with deep learning framework, these kinds of unique sentiment information are not fully used in sentiment analysis tasks. Moreover, the sentiment analysis task can be seen as a sequence model, and the sequence model has a problem: the model will decode the input file sequences into a specific length vector. If the length of the vectors is set too short, the input text information will be lost, and finally the text will be misjudged. To solve these problems, we propose a bidirectional LSTM model with self-attention mechanism and multi-channel features (SAMF-BiLSTM). The method models the existing linguistic knowledge and sentiment resources in sentiment analysis tasks to form different feature channels, and uses self-attention mechanism to enhance the sentiment information. SAMF-BiLSTM model can fully exploit the relationship between target words and sentiment polarity words in a sentence, and does not rely on manually organized sentiment lexicon. In addition, we propose the SAMF-BiLSTM-D model based on SAMF-BiLSTM model for document-level text classification tasks. The method obtains the representation of all sentences in the document through SAMF-BiLSTM training, then integrates BiLSTM to learn the representation of all sentences, and further obtains the sentiment feature information of the entire document. Finally, we evaluate experiment results under five datasets. The results show that SAMF-BiLSTM and SAMF-BiLSTM-D are superior to other advanced methods in classification accuracy in most cases.

A mixed approach of statistical weighting method and unsupervised method to improve Uyghur sentiment classification

Considering the scarcity of Uyghur sentiment resources, in this paper proposed a new combined unsupervised sentiment classification method for Uyghur text without any labeled corpora. In the first part, a Uyghur sentiment dictionary, UYSentiDict, was adopted to classify the sentences. For the sentiment vocabulary matching, both the matching of the original word and the stem were considered, and the influence of sentence patterns, negation words, and degree adverbs were further considered as well. Based on different thresholds, the sentences with higher sentiment values were selected from the lexicon-based classification results as a pseudo-labeled dataset. In the second part, different sentiment characteristics were learned from the pseudo-labeled dataset by the machine learning classifier, and the remaining categorical data were further classified. It can be concluded that the method proposed in this paper has good classification efficiency in Uyghur sentiment corpora in four different fields, and some results were performed better than the classification results of machine learning classifier. Moreover, this method is not restricted by the field of data and does not need to be marked in advance with good training corpus, and can solve the resource shortage problem in the field of Uyghur sentiment classification effectively.

A Survey of Opinion Mining in Arabic

Opinion-mining or sentiment analysis continues to gain interest in industry and academics. While there has been significant progress in developing models for sentiment analysis, the field remains an active area of research for many languages across the world, and in particular for the Arabic language, which is the fifth most-spoken language and has become the fourth most-used language on the Internet. With the flurry of research activity in Arabic opinion mining, several researchers have provided surveys to capture advances in the field. While these surveys capture a wealth of important progress in the field, the fast pace of advances in machine learning and natural language processing (NLP) necessitates a continuous need for a more up-to-date literature survey. The aim of this article is to provide a comprehensive literature survey for state-of-the-art advances in Arabic opinion mining. The survey goes beyond surveying previous works that were primarily focused on classification models. Instead, this article provides a comprehensive system perspective by covering advances in different aspects of an opinion-mining system, including advances in NLP software tools, lexical sentiment and corpora resources, classification models, and applications of opinion mining. It also presents future directions for opinion mining in Arabic. The survey also covers latest advances in the field, including deep learning advances in Arabic Opinion Mining. The article provides state-of-the-art information to help new or established researchers in the field as well as industry developers who aim to deploy an operational complete opinion-mining system. Key insights are captured at the end of each section for particular aspects of the opinion-mining system giving the reader a choice of focusing on particular aspects of interest.

Various Aspects of Sentiment Analysis: A Review

Sentiment Analysis (SA) is the utmost progressive research field in natural language processing that focuses on identifying the emotions of a writer behind the series of words and classifies public attitude, opinion from visuals, audios and texts. This paper categorizes and presents a refined study of recently published articles related with sentiment analysis research. It demonstrates an outline of the prime issues and challenges, explores sentiment resources and discusses practical techniques and tools practiced in this context. It also provides an insight of various sentiment analysis applications in several different scenarios, current enhancements and describes the possible future directions for deeper understanding of human emotions.

An automatic non-English sentiment lexicon builder using unannotated corpus

Sentiment lexicons in the English language are widely accessible while in many other languages, these resources are extremely deficient. Current techniques and methods for sentiment analysis focus mainly on the English language, whereas other languages are neglected due to lack of resources. In order to overcome challenges faced in building non-English lexicons, we propose a language-independent method that automatically builds non-English sentiment lexicons based on currently available English lexicons with an unannotated corpus from the target language. The proposed method will automatically recognize and extract new polarity words from the unannotated corpus based on the initial seed lexicons that are developed by translating three reliable English lexicons. The experimental results from the test datasets confirmed that a developed non-English sentiment lexicon could significantly enhance the performance of non-English sentiment classifications, compared with other methods and lexicons. The developed lexicon in the Arabic language outperformed other commonly used methods for developing non-English lexicons, with an 0.74 F measure. The adopted approach in this study was proven to be language independent and can be implemented in other languages as well. This paper also contributes to understanding the approaches to developing sentiment resources.

Learning from context: A mutual reinforcement model for Chinese microblog opinion retrieval

This study addresses the problem of Chinese microblog opinion retrieval, which aims to retrieve opinionated Chinese microblog posts relevant to a target specified by a user query. Existing studies have shown that lexicon-based approaches employed online public sentiment resources to rank sentimentwords relying on the document features. However, this approach could not be effectively applied to microblogs that have typical user-generated content with valuable contextual information: “user–user” interpersonal interactions and “user–post/comment” intrapersonal interactions. This contextual information is very helpful in estimating the strength of sentiment words more accurately. In this study, we integrate the social contextual relationships among users, posts/comments, and sentiment words into a mutual reinforcement model and propose a unified three-layer heterogeneous graph, on which a random walk sentiment word weighting algorithm is presented to measure the strength of opinion of the sentiment words. Furthermore, the weights of sentiment words are incorporated into a lexicon-based model for Chinese microblog opinion retrieval. Comparative experiments are conducted on a Chinese microblog corpus, and the results show that our proposed mutual reinforcement model achieves significant improvement over previous methods.

Using Chinese radical parts for sentiment analysis and domain-dependent seed set extraction

Although there has been good progress in English sentiment analysis and resources, studies in English cannot be directly used in Chinese owing to the nature of Chinese language. Previous studies suggested adopting linguistic information, such as grammar and morpheme information, to assist in sentiment analysis for Chinese text. However, morpheme-based approaches have a problem in identifying seeds. In addition, these methods do not take advantage of radicals in the characters, which contain a great deal of semantic information. A Chinese word is composed of one or more characters, each of which has its radical part. We can interpret the partial meaning of a character by analyzing that of the radical in the character. Therefore, we not only consider the radical information as the semantic root of a character, but also consider the radical parts between characters in a word as an appropriate linguistic unit for conducting sentiment analysis.In this study, we conducted a series of experiments using radicals as the feature unit in sentiment analysis. Using segmented results from part-of-speech tools as a meaningful linguistic unit (word) in Chinese, we conducted analyses of single-feature word (unigram) and frequently seen two words (pointwise mutual information collocated bigrams) through various sentiment analysis measures. It is concluded that radical features could work better than word features and would consume less computing memory and time. An extended study of the extraction of seeds was also conducted, and the results indicated that 50 seed radical features performed well. A cross-corpus comparison was also conducted; the results demonstrated that the use of 50 extracted radical features as domain-dependent keywords worked better than other sentiment analysis strategies. This study confirmed that radical information could be adopted as a feature unit in sentiment analysis and that domain-dependent radicals could be reused in different corpora.

Arabic senti-lexicon: Constructing publicly available language resources for Arabic sentiment analysis

Sentiment analysis is held to be one of the highly dynamic recent research fields in Natural Language Processing, facilitated by the quickly growing volume of Web opinion data. Most of the approaches in this field are focused on English due to the lack of sentiment resources in other languages such as the Arabic language and its large variety of dialects. In most sentiment analysis applications, good sentiment resources play a critical role. Based on that, in this article, several publicly available sentiment analysis resources for Arabic are introduced. This article introduces the Arabic senti-lexicon, a list of 3880 positive and negative synsets annotated with their part of speech, polarity scores, dialects synsets and inflected forms. This article also presents a Multi-domain Arabic Sentiment Corpus (MASC) with a size of 8860 positive and negative reviews from different domains. In this article, an in-depth study has been conducted on five types of feature sets for exploiting effective features and investigating their effect on performance of Arabic sentiment analysis. The aim is to assess the quality of the developed language resources and to integrate different feature sets and classification algorithms to synthesise a more accurate sentiment analysis method. The Arabic senti-lexicon is used for generating feature vectors. Five well-known machine learning algorithms: naïve Bayes, k-nearest neighbours, support vector machines (SVMs), logistic linear regression and neural network are employed as base-classifiers for each of the feature sets. A wide range of comparative experiments on standard Arabic data sets were conducted, discussion is presented and conclusions are drawn. The experimental results show that the Arabic senti-lexicon is a very useful resource for Arabic sentiment analysis. Moreover, results show that classifiers which are trained on feature vectors derived from the corpus using the Arabic sentiment lexicon are more accurate than classifiers trained using the raw corpus.