Sentiment Lexicon Research Articles

Sentiments Analysis and Text Summarization of Medicine Reviews Using Deep Sequential Iterative Model with Attention Encoder

In this day and age of data innovation, vast amounts of data are being generated daily, much of it is useless to the general public unless it is correctly handled. This year has seen a huge increase in social networks’ relevance, resulting in vast quantities of data provided by users. This vast amount of information is gathered from a variety of sources, including company websites, customer blogs, and item reviews. The text outline is probably the most significant point of view in daily life. Analysis of text sentiment is a method for assessing, summarising, and drawing conclusions about the most important content. In the field of sentiment analysis, attention methods have been crucial since they make use of sentiment lexicons to gather a huge amount of sentiment polarity information. With an attention mechanism that acts as a link between linguistic information with a strong emotional component and deep learning algorithms, it may be possible to boost text sentiment significantly. In the LSTM model, word sequence addictions can be captured over the long term. For the first time, scientists have developed an attention model that combines LSTM with an incredibly deep RNN model to tackle the problem of sentiment analysis in the real world. The iterative method trains the first set of word embeddings using a Word to Vector technique. With the Word2Vec algorithm, text strings are converted into a numerical value vector and distance between words and comparative words based on meaning are calculated. Using the attention strategy has the added benefit of potentially enhancing machine learning’s ability to learn sentiment representations. The attention model is more scalable and adaptable than previous approaches. The major objective of this work is to assess feelings and develop an abstracted content outline, decide on the semantic summarization of different materials and effectively process the data.

Building a New Tourism Sentiment Lexicon Containing Descriptive Words in Modern Standard and Colloquial Arabic

Building a New Tourism Sentiment Lexicon Containing Descriptive Words in Modern Standard and Colloquial Arabic

An analysis of public opinions regarding Internet-famous food: a 2016–2019 case study on Dianping

PurposeInternet-famous food (also known as “online celebrity” food) is very popular in the digital age. This study aims to investigate consumer attitudes and understand consumer behavior towards Internet-famous food.Design/methodology/approachThe authors collected 136,835 online comments regarding “Internet-famous food” from Dianping platform between 2016 and 2019 using a web scraper. A sentiment lexicon for Internet-famous food was constructed, and sentiment analysis is further conducted to understand consumer attitudes. Additionally, the authors use topic analysis and time series analysis to study consumer behavior.FindingsSentiment analysis showed that the number of consumers' comments decreased over time with the attitudes being overall positive, and the Internet-famous food industry has a positive prospect; time series analysis showed that the consumption of Internet-famous food was not affected by the season; topic analysis showed that consumers' comments on Internet-famous food were rich with a large variety, covering food categories, brand, quality, service, environment and price.Originality/valueTo the authors’ knowledge, limited research has focused on public opinions regarding “Internet-famous food”. This is the first study on consumer behavior towards Internet-famous food. This article provides a unique insight into the purchasing behavior and attitude of Chinese Internet-famous food consumers through text mining.

Predicting the Success of Internet Social Welfare Crowdfunding Based on Text Information

This study explored how the success of project crowdfunding can be predicted based on the texts of Internet social welfare crowdfunding projects. Through a calculation of the quantity of information and a mining of the sentimental value of the text, how the text information of the interconnected social welfare crowdfunding project affects the success of the project was studied. To this aim, a sentimental dictionary of Chinese Internet social welfare crowdfunding texts was constructed, and information entropy was used to calculate the quantity of information in the text. It was found that, compared with the information presented in the text, the fundraiser’s social network factors are key in improving the success of fundraising. The sentimental value of the text positively affects the success of fundraising, while the influence of the quantity of information is represented as an inverted, U-shaped relationship. The non-ideal R-squared indices reflected that the multiple linear regression models do not perform well regarding this prediction. Furthermore, this paper validated and analyzed the prediction efficiency of four machine-learning models, including a multiple regression model, a decision tree regression model, a random forest regression model, and an AdaBoost regression model, and the AdaBoost regressor showed the best efficiency, with an accuracy R2 of up to 97.7%. This study provides methods for the quantified processing of information contained in social welfare crowdfunding texts, as well as effective prediction on social welfare crowdfunding, and also seeks to raise the success rate of crowdfunding and thus features commercial and social value.

A risky large group emergency decision-making method based on topic sentiment analysis

This study proposes a decision-making method based on topic sentiment analysis to address the problem of completely data-driven attribute information acquisition and risk control of the intuitionistic fuzzy preference in large group emergency decision-making. First, Latent Dirichlet Allocation (LDA) topic mining is applied to rank public topics and construct an emergency sentiment dictionary for topic sentiment analysis. The attribute system structure and weight information of large group emergency decision-making can be obtained by transforming the high-concern topic sentiment value. Second, with the public attention attribute and public attribute preference as references for large group emergency decision-making, risk measurement under the intuitionistic fuzzy preference model is based on risk credibility. The risk–consensus feedback mechanism of large group emergency decision-making is designed to obtain the high-consensus and low-risk alternative. Finally, the applicability and effectiveness of the method are demonstrated using a case study involving a serious explosion accident.

Sentiment Analysis of English Text with Multilevel Features

In natural language processing, text sentiment analysis is one of the important branches. It refers to the use of text mining and other technologies to extract attitudes, opinions, and other information from texts containing emotional information for analysis. Traditional sentiment analysis methods can be roughly divided into two categories: one is dictionary-based methods, and the other is machine learning-based methods. The former relies on the quality of the sentiment dictionary, while the latter relies on a large amount of high-quality data, so both have certain limitations. In text sentiment analysis research, word-level and sentence-level sentiment information extraction is a basic research task and has important research value. Through research, it is found that domain knowledge and context are two important factors influencing the extraction of emotional information. To this end, this paper proposes a text sentiment analysis method that integrates multiple features and constructs three features, which are based on the sentiment value feature of the dictionary, the expression feature, and the improved semantic feature, which are combined to build a text sentiment classification model. Aiming at the colloquial, irregular, and diverse features of English social media texts, this paper proposes a multilevel feature representation method. The sentiment classification experiments on English text show that the multilevel features proposed in this paper can effectively improve the F1_macro and accuracy of multiple model classifications. Compared with the existing research, the model in this paper improves the effect the most obvious.

Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach.

The mobile internet has resulted in intimate partner violence (IPV) events not being viewed as interpersonal and private issues. Such events become public events in the social network environment. IPV has become a public health issue of widespread concern. It is a challenge to obtain systematic and detailed data using questionnaires and interviews in traditional Chinese culture, because of face-saving and the victim’s shame factors. However, online comments about specific IPV events on social media provide rich data in understanding the public’s attitudes and emotions towards IPV. By applying text mining and sentiment analysis to the field of IPV, this study involved construction of a Chinese IPV sentiment dictionary and a complete research framework. We analyzed the trends of the Chinese public’s emotional evolution concerning IPV events from the perspectives of a time series as well as geographic space and social media. The results show that the anonymity of social networks and the guiding role of opinion leaders result in traditional cultural factors such as face-saving and family shame for IPV events being no longer applicable, leading to the spiral of an anti-silence effect. Meanwhile, in the process of public emotional communication, anger often overwhelms reason, and the spiral of silence remains in effect in social media. In addition, there are offensive words used in the IPV event texts that indicate misogyny in emotional, sexual, economic and psychological abuse. Fortunately, mainstream media, as crucial opinion leaders in the social network, can have a positive role in guiding public opinion, improving people’s ability to judge the validity of network information, and formulating people’s rational behaviour.

Exploring Bidirectional Performance of Hotel Attributes through Online Reviews Based on Sentiment Analysis and Kano-IPA Model

As people increasingly make hotel booking decisions relying on online reviews, how to effectively improve customer ratings has become a major point for hotel managers. Online reviews serve as a promising data source to enhance service attributes in order to improve online bookings. This paper employs online customer ratings and textual reviews to explore the bidirectional performance (good performance in positive reviews and poor performance in negative reviews) of hotel attributes in terms of four hotel star ratings. Sentiment analysis and a combination of the Kano model and importance-performance analysis (IPA) are applied. Feature extraction and sentiment analysis techniques are used to analyze the bidirectional performance of hotel attributes in terms of four hotel star ratings from 1,090,341 online reviews of hotels in London collected from TripAdvisor.com (accessed on 4 January 2022). In particular, a new sentiment lexicon for hospitality domain is built from numerous online reviews using the PolarityRank algorithm to convert textual reviews into sentiment scores. The Kano-IPA model is applied to explain customers’ rating behaviors and prioritize attributes for improvement. The results provide determinants of high/low customer ratings to different star hotels and suggest that hotel attributes contributing to high/low customer ratings vary across hotel star ratings. In addition, this paper analyzed the Kano categories and priority rankings of six hotel attributes for each star rating of hotels to formulate improvement strategies. Theoretical and practical implications of these results are discussed in the end.

MELex: The Construction of Malay-English Sentiment Lexicon

Currently, the sentiment analysis research in the Malaysian context lacks in terms of the availability of the sentiment lexicon. Thus, this issue is addressed in this paper in order to enhance the accuracy of sentiment analysis. In this study, a new lexicon for sentiment analysis is constructed. A detailed review of existing approaches has been conducted, and a new bilingual sentiment lexicon known as MELex (Malay-English Lexicon) has been generated. Constructing MELex involves three activities: seed words selection, polarity assignment, and synonym expansions. Our approach differs from previous works in that MELex can analyze text for the two most widely used languages in Malaysia, Malay, and English, with the accuracy achieved, is 90%. It is evaluated based on the experimentation and case study approaches where the affordable housing projects in Malaysia are selected as case projects. This finding has given an implication on the ability of MELex to analyze public sentiments in the Malaysian context. The novel aspects of this paper are two-fold. Firstly, it introduces the new technique in assigning the polarity score, and second, it improves the performance over the classification of mixed language content.

Intelligent Classification Model of Music Emotional Environment Using Convolutional Neural Networks.

The majority of traditional text sentiment classification techniques rely on machine learning or sentiment dictionaries, but these approaches have the drawback of sparse data and ignore word semantics and word order information. A convolutional neural network- (CNN-) based music emotion classification model is proposed in this paper to address the aforementioned issues. The model in this paper has clear advantages in every way. On the same dataset, the model in this study has an average accuracy of 91.4 percent, while LeNet, AlexNet, and VGGNet have accuracy averages of 75.3 percent, 72.2 percent, and 79.4 percent, respectively. The error value of the other three algorithms is higher than the cost function value because people's emotions in the cognitive field are divided into different categories. However, in the field of music emotion retrieval, we can only extract the features of the known melody and then search for the same emotion, so we need to build a computerized music emotion classifier if we want to find emotions that are similar to a particular melody. This study examines musical emotion models that already exist, then extracts musical emotion features, and builds a musical emotion classifier using a neural network. The classifier is then further trained until the error classification rate of the training samples is within a certain error range, after which the classification results are marked by pertinent feedback.

A textual analysis of the US Securities and Exchange Commission's accounting and auditing enforcement releases relating to the Sarbanes–Oxley Act

SummaryWe focus on textual analysis of the US Securities and Exchange Commission's accounting and auditing enforcement releases (AAERs). Our research question is: Did the Sarbanes–Oxley Act (SOX) 2002 affect the qualitative linguistic content of the AAERs in the post‐SOX period? To answer this question, we test the null hypotheses that there will be no differences in the qualitative verbiage and sentiment of AAERs in the time periods that we study related to the enactment of SOX: pre‐SOX and post‐SOX. To resolve the research question, we applied several text mining methods and classification machine‐learning methods. We first used two basic text‐mining methods, generating a bag of words and topic modeling, for descriptive analysis of the AAER content before the enactment of SOX and after the enforcement of SOX. We then conducted sentiment analysis using four sentiment dictionaries on the content of the two time periods: before SOX and after SOX. Finally, we developed three different classification models based on well‐known supervised learning algorithms and determined that SOX‐related AAERs could be distinguished from non‐SOX‐related AAERs. Based on the results, we conclude that there were significant linguistic differences in the AAER content of the post‐SOX period compared with the pre‐SOX period. In other words, post‐SOX‐related AAERs are qualitatively different in terms of linguistic contents and sentiment values compared with the non‐SOX‐related AAERs.

A Deep Learning Model for Opinion mining in Twitter Combining Text and Emojis

Several approaches have been proposed to study opinions on Social Network Sites (SNS). Unfortunately, those works are not topic-sensitive and do not investigate the impact of emojis on text-based classification. In this paper, we propose a novel approach to predict the users’ opinions expressed through textual tweets and emojis. Thus, we construct an emoji sentiment lexicon. Then, we extract opinions from the text before considering both the text and emojis to see how they enhance the expression of opinions in SNS discussions. We conduct a set of benchmarks using several well-known machine learning algorithms, leading to an accuracy of 83, 7%.

Automatic Sentiment Annotation of Idiomatic Expressions for Sentiment Analysis Task

Users of social media may use words and phrases literally to convey their views or opinion clearly. However, some people choose to utilise idioms or proverbs that are implicit and indirect in order to make a stronger impression on the audience or perhaps to catch their attention by utilising a funny, sarcastic, or metaphorical phrases. Idioms and proverbs are examples of figurative expressions with a thematically coherent totality that cannot be understood literally. In a previous work, the extension of IBM’s Sentiment Lexicon of Idiomatic Expressions was proposed to include around 9,000 idioms; both lexicons are manually annotated by crowdsourcing service. Therefore, in this research, we provide knowledge-based expansion approach to avoid human annotation of idioms. For sentiment classification, the proposed method has the advantage that it does not require any fine-tuning for the BERT model. Experimental comparisons show that the automated idiom enrichment and annotation are very beneficial for the performance of the sentiment classifier. The expanded annotated lexicon will be made available to the general public.

Exploring E-Commerce Product Experience Based on Fusion Sentiment Analysis Method

With the speedy development of e-commerce, a growing number of customers tend to share their subjective perceptions of the product or service on the Internet. This phenomenon makes the commercial value of online reviews increasingly prominent. In this context, how to gain insights into consumers’ perceptions and attitudes from massive comments has become a hot-button topic. Addressing this requirement, this paper developed a fusion sentiment analysis method combining textual analysis techniques with machine learning algorithms, aiming to mine online product experience. The method mainly consists of three steps. Firstly, inspired by the sensitivity of sentiment dictionary to emotional information, we utilize the dictionary to extract sentiment features. Afterward, the SVM algorithm is adopted to identify sentiment polarities of reviews. Based on this, sentiment topics are extracted from reviews through the LDA model. Furthermore, to avoid the omission of emotional information, the dictionary is extended based on semantic similarity. Meanwhile, in this research, the fact that words in reviews have unequal sentiment contribution, which has been neglected in existing studies, is taken into account. Specifically, we introduce the weighting method to measure the sentiment contribution. Finally, the investigation of consumers’ reading experiences of online books on Amazon has verified the feasibility and validity of the method. The results demonstrate that the method accurately determines reviews’ emotional tendencies and captures elements affecting reading experiences from reviews. Overall, the research provides an effective way to mine online product experience and track customers’ demands, thereby strongly supporting future product improvement and marketing strategy optimization.

Research on traditional Mongolian sentiment analysis combined with prior knowledge

In order to solve some problems of traditional machine learning algorithms in Mongolian sentiment analysis tasks, such as low accuracy, few sentiment corpus, and poor training effect, a Traditional Mongolian sentiment classification algorithm integrates prior knowledge is proposed. First and foremost, 1.3 million unlabeled Mongolian corpora are pre-trained and preliminarily segmented to obtain basic Mongolian vocabulary information. Secondly, the regularization method is used to segment the corpus. Next, a Mongolian text sentiment dictionary is created, which is used as prior knowledge. At the same time, an attention mechanism is integrated into the model to obtain emotional features in a dynamic form. In the last step, based on the Mongolian sentiment data set, the model is further trained and slightly modified to obtain the final traditional Mongolian sentiment analysis model. From the experimental results, the proposed model, compared with the classical sentiment classification algorithms such as Fasttext, BiLSTM, and CNN, has stronger feature extraction ability, sentiment classification performance, and faster convergence speed.

Opinion mining of online product reviews using a lexicon-based algorithm

The worldwide social media is a rich resource of user-generated data, which can help organizations to formulate their business strategies, or affect the process of decision making in product or service design and implementation. The focus of this thesis is on extraction and analysis of unstructured product reviews for training predictive models, which recognize a specific range of human affective states. These affective states include emotions, moods, opinions, attitudes, as well as continuous dimensions for sentiment characterization, such as valence or intensity. Based on the sentiment analysis, the task is to build a domain-specific sentiment dictionary of English words form the product posts and comments, and to propose and test of lexicon-based algorithm to predict the user opinions.

A Study of the Application of Weight Distributing Method Combining Sentiment Dictionary and TF-IDF for Text Sentiment Analysis

The most commonly used methods in text sentiment analysis are rule-based sentiment dictionary and machine learning, with the later referring to the use of vectors to represent text followed by the use of machine learning to classify the vectors. Both methods have their limitations, including inflexibility of rules, non-prominence of sentiment words. In this paper, we design a weight distributing method combining the two methods for text sentiment analysis, by which the sentence vectors obtained can both highlight words with sentiment meanings while retaining their text information. Empirical results show that based on this new method, the accuracy rate of text sentiment analysis can reach as high as 82.1%, which means 13.9% higher than rule-based sentiment dictionary method, and 7.7% higher than TF-IDF weighting method.

Does the emotional tendency of COVID-19 news affect financial markets?

The influence of internet financial news on financial markets has been verified from multiple perspectives. This paper examines whether the emotional tendency of COVID-19 news affect financial markets. Based on constructing the sentiment dictionary of COVID-19 news, we measure the sentiment indicators of COVID-19 news, and then study the dynamic relationship between the emotional tendency of COVID-19 news and the rate of return in financial markets. Compared with the news before the outbreak of COVID-19, our results show that the emotional tendency of COVID-19 news does affect the rate of return in the stock and bond markets.

Sentiment Dictionary for Business Cycle Analysis and its Applications

Sentiment Dictionary for Business Cycle Analysis and its Applications

Language and Domain Specificity: A Chinese Financial Sentiment Dictionary

Abstract We use Word2vec to develop a financial sentiment dictionary from 3.1 million Chinese-language financial news articles. Our dictionary maps semantically similar words to a subset of human-expert generated financial sentiment words. In validation tests, our dictionary scores the sentiment of articles consistently with human reading of full articles. In return association tests, our dictionary outperforms and subsumes previous Chinese financial sentiment dictionaries, such as direct translations of Loughran and McDonald’s (2011, Journal of Finance, 66, 35–65) English-language financial dictionary. We also generate a list of politically related positive words that is unique to China; we find that this list has a weaker association with returns than does the list of other positive words. We demonstrate that state media uses more politically related positive and fewer negative words, and exhibits a sentiment bias. This bias renders the state media’s sentiment as less return-informative. Our findings demonstrate that dictionary-based sentiment analysis exhibits strong language and domain specificity.