Sentiment Words Research Articles

Text‐based soybean futures price forecasting: A two‐stage deep learning approach

AbstractThis paper investigates the soybean futures price prediction problem from a new perspective and proposes an effective prediction model named Two‐Stage Hybrid Long Short‐Term Memory (TSH‐LSTM) by using text data from social media. First, the unstructured text is transformed into structured data by sentiment analysis and text classification methods. The improved sentiment score is computed by combining the degree centrality of sentiment words based on the sentiment dictionary method, and the characteristics of price fluctuations in texts are learned through the text Recurrent Convolutional Neural Networks. Second, the significant relationship between social media features and soybean futures price is assessed through stepwise regression, and the results of such an assessment are used as a basis for the identification of significant factors as input variables of the prediction model. Finally, the TSH‐LSTM prediction model is designed, and the final prediction result is acquired through the combination of prediction results of each stage using the error reciprocal method. The empirical results indicate that the incorporation of the social media text feature helps improve forecasting performances. Specifically, the proposed TSH‐LSTM is more accurate than univariate LSTM, multivariate LSTM, and eXtreme Gradient Boosting.

How has airport service quality changed in the context of COVID-19: A data-driven crowdsourcing approach based on sentiment analysis

Airport service quality (ASQ) is a competitive advantage for airport management in today's airport market. Since the COVID-19 health crisis has unprecedentedly influenced airport regulations and operations, effective measurement of ASQ has become crucial for airport administrations. Surveying travelers' attitudes is useful for ASQ assessment but collecting responses could be time-consuming and costly. Therefore, this paper adopts a data-driven crowdsourcing approach to study ASQ during the COVID-19 pandemic by investigating Google Maps reviews from the 98 busiest U.S. airports. To do so, this study develops a topical ontology of keywords regarding ASQ attributes and uses a sentiment tool to derive passengers' attitudes. Through sentiment analysis, Google Maps reviews show more positive sentiment toward environment and personnel but remain constant about facilities during COVID-19. The lexical salience-valence analysis (LSVA) is then applied to explain such changes by tracking the sentiment of frequent words in reviews. Through correlation and regression analysis, this study demonstrates that rating is significantly related to check-in, environment, and personnel in pre-and post-COVID periods. Additionally, the effect of access, wayfinding, facilities, and environment on rating significantly differs between the two periods. The findings illustrate the effectiveness of leveraging online reviews and offer practical implications for what matters to air travelers, especially in the COVID-19 context.

Sentiment Analysis of Chinese Reviews Based on BiTCN-Attention Model

It is of great significance for individuals, enterprises, and government departments to analyze and excavate the sentiment in the comments. Many deep learning models are used for text sentiment analysis, and the BiTCN model has good efficacy on sentiment analysis. However, in the actual semantic expression, the contribution of each word to the sentiment tendency is different, BiTCN treats it fairly and does not pay more attention to the key sentiment words. For this problem, a sentiment analysis model based on the BiTCN-Attention is proposed in this paper. The Self-Attention mechanism and Multi-Head Self-Attention mechanism are added to BiTCN respectively to form BiTCN-SA and BiTCN-MHSA, which improve the weight of sentiment words and the accuracy of feature extraction, to increase the effect of sentiment analysis. The experimental results show that the model accuracies of BiTCN-SA and BiTCN-MHSA in the JingDong commodity review data set are 3.96% and 2.41% higher than that of BiTCN, respectively. In the comment data set of DianPing, the accuracy of BiTCN-SA and BiTCN-MHSA improved by 4.62% and 3.49%, respectively, compared with that of BiTCN.

Refining Word Embeddings with Sentiment Information for Sentiment Analysis

Natural Language Processing problems generally require the use of pre-trained distributed word representations to be solved with deep learning models. However, distributed representations usually rely on contextual information which prevents them from learning all the important word characteristics. The task of sentiment analysis suffers from such a problem because sentiment information is ignored during the process of learning word embeddings. The performance of sentiment analysis can be affected since two words with similar vectors may have opposite sentiment orientations. The present paper introduces a novel model called Continuous Sentiment Contextualized Vectors (CSCV) to address this problem. The proposed model can learn word sentiment embedding using its surrounding context words. It uses Continuous Bag-of-Words (CBOW) model to deal with the context and sentiment lexicons to identify sentiment. Existing pre-trained vectors are combined then with the obtained sentiment vectors using Principal component analysis (PCA) to enhance their quality. The experiments show that: (1) CSCV vectors can be used to enhance any pre-trained word vectors; (2) The result vectors strongly alleviate the problem of similar words with opposite polarities; (3) The performance of sentiment classification is improved by applying this approach.

How Incongruence Between Managers’ Tone of Voice and Word Sentiment Influences Security Analysts

Maintaining favorable analyst recommendations is important to organizations and their management, as these recommendations provide stakeholders with expert opinions about organizations’ well-being. Hence, there is a strong impetus for managers to prevent analysts downgrading their organization. In verbal communication, such as in earnings calls, managers not only try to influence analysts via the words that they use, but also via the tone in which they speak (i.e., paraverbal communication). Paraverbal communication is both an extra channel of information for analysts, and a strategic tool managers may use to exert influence. On the one hand, management wants to signal trustworthiness through congruency in their communication, but on the other hand is inclined to offset negative information by speaking positively, or downplay expectations by using a negative tone when discussing positive information. Leveraging several state-of-the-art Natural Language Processing algorithms, using earnings calls’ audio and textual data, we analyze the incongruence in managers’ communication. We find that paraverbal communication affects analysts’ recommendations and show which paraverbal strategies characterize organizations that are least likely to get downgraded. Our study therefore contributes to the literature on corporate communication, demonstrating the importance of verbal tone in managing the relationship with market intermediaries (analysts). Furthermore, this is one of the first studies in management that utilizes audio data quantitatively.

ASK-RoBERTa: A pretraining model for aspect-based sentiment classification via sentiment knowledge mining

The main objective of aspect-based sentiment classification (ABSC) is to predict sentiment polarities of different aspects from sentences or documents. Recent research integrates sentiment terms into pretraining models whose accuracy impacts the ABSC performance. This paper introduces a sentiment knowledge-adaptive pretraining model (ASK-RoBERTa). A sentiment word dictionary is first built from general and field sentiment words. We develop a series of term and sentiment mining rules based on part-of-speech tagging and sentence dependency grammar. These mining rules consider word dependencies, compounding, and conjunctions. The pretraining model optimizes the mining rules to capture the dependency between aspects and sentiment words. Experimental results on multiple public benchmark datasets demonstrate the satisfactory performance of ASK-RoBERTa.

Linguistic features and psychological states: A machine-learning based approach.

Previous research mostly used simplistic measures and limited linguistic features (e.g., personal pronouns, absolutist words, and sentiment words) in a text to identify its author’s psychological states. In this study, we proposed using additional linguistic features, that is, sentiments polarities and emotions, to classify texts of various psychological states. A large dataset of forum posts including texts of anxiety, depression, suicide ideation, and normal states were experimented with machine-learning algorithms. The results showed that the proposed linguistic features with machine-learning algorithms, namely Support Vector Machine and Deep Learning achieved a high level of performance in the detection of psychological state. The study represents one of the first attempts that uses sentiment polarities and emotions to detect texts of psychological states, and the findings may contribute to our understanding of how accuracy may be enhanced in the detection of various psychological states. Significance and suggestions of the study are also offered.

Sentiment Analysis in British and American Literature Teaching under Formative Assessment and Machine Learning

In English education, British and American literature is a new type of course. The teaching of British and American literature has also undergone many reforms. In the practice of teaching reform, artificial intelligence- (AI-) assisted teaching such as machine learning (ML) has a long history. The performance is continuously improved by studying the mechanism of computer simulation of the human brain learning British and American literature. Then, computer intelligence can be realized. Based on this, this paper mainly discusses two aspects. One is the sentiment tendency analysis method based on the sentiment dictionary, and the other is the sentiment tendency analysis method based on ML. It mainly introduces the judgment of different emotional tendencies by the sentiment analysis model, which is an automatic review analysis and ensemble classification approach. The improvement of sentiment analysis improves the recognition range of text sentiment words in British and American literature teaching to optimize the process of text analysis. Its main feature is that the sentiment analysis of text directly acts on the tendency of words, with fine granularity and accurate analysis. Finally, it is concluded that the maximum value of the algorithm proposed here is 0.9, which has higher accuracy than the maximum value of 0.81 of other analysis models. The results indicate that the integrated classification model combining British and American literature teaching with the dimensions hidden Markov model has relatively reasonable text analysis and high sentiment classification accuracy. In terms of British and American literature teaching, using ML algorithms can effectively help teachers teach British and American literature through sentiment analysis.

Sentiment Analysis of Chinese E-Commerce Product Reviews Using ERNIE Word Embedding and Attention Mechanism

The development of e-commerce has ushered in a golden age. E-commerce product reviews are remarks initiated by online shopping users to evaluate the quality and service of the products they purchase; these reviews help consumers learn the reality of the product. The sentiment polarity of e-commerce product reviews is the best way to obtain customer feedback on products. Therefore, sentiment analysis of product reviews on e-commerce platforms is greatly significant. However, the challenges of sentiment analysis of Chinese e-commerce product reviews lie in dimension mapping, disambiguation of sentiment words, and polysemy of Chinese words. To solve the above problems, this paper proposes a sentiment analysis model ERNIE-BiLSTM-Att (EBLA). Here, the dynamic word vector generated using the Enhanced Representation through Knowledge Integration (ERNIE) word embedding model is input into the Bidirectional Long Short-term Memory (BiLSTM) to extract text features. Then, the Attention Mechanism (Att) is used to optimize the weight of the hidden layer. Finally, softmax is used as the output layer for sentiment classification. The experimental results on the JD.com Chinese e-commerce product review dataset show that the proposed model achieves more than 0.87 in precision, recall, and F1 values, which is superior to classic deep learning models proposed by other researchers; it has strong practicability in sentiment analysis of Chinese e-commerce product reviews.

The language of happiness in self-reported descriptions of happy moments: Words, concepts, and entities

This article attempts to study the language of happiness from a double perspective. First, the impact and relevance of sentiment words and expressions in self-reported descriptions of happiness are examined. Second, the sources of happiness that are mentioned in such descriptions are identified. A large sample of “happy moments” from the HappyDB corpus is processed employing advanced text analytics techniques. The sentiment analysis results reveal that positive lexical items have a limited role in the description of happy moments. For the second objective, unsupervised machine learning algorithms are used to extract and cluster keywords and manually label the resulting semantic classes. Results indicate that these classes, linguistically materialized in compact lexical families, accurately describe the sources of happiness, a result that is reinforced by our named entities analysis, which also reveals the important role that commercial products and services play as a source of happiness. Thus, this study attempts to provide methodological underpinnings for the automatic processing of self-reported happy moments, and contributes to a better understanding of the linguistic expression of happiness, with interdisciplinary implications for fields such as affective content analysis, sentiment analysis, and cultural, social and behavioural studies.

Stepwise Multi-Task Learning Model for Holder Extraction in Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis is a text analysis technique that categorizes data by aspect and identifies the sentiment attributed to each one and a task for a fine-grained sentiment analysis. In order to accurately perform a fine-grained sentiment analysis, a sentiment word within a text, a target it modifies, and a holder who represents the sentiment word are required; however, they should be extracted in sequence because the sentiment word is an important clue for extracting the target, which is key evidence of the holder. Namely, the three types of information sequentially become an important clue. Therefore, in this paper, we propose a stepwise multi-task learning model for holder extraction with RoBERTa and Bi-LSTM. The tasks are sentiment word extraction, target extraction, and holder extraction. The proposed model was trained and evaluated under Laptop and Restaurant datasets in SemEval 2014 through 2016. We have observed that the performance of the proposed model was improved by using stepwised features that are the output of the previous task. Furthermore, the generalization effect has been observed by making the final output format of the model a BIO tagging scheme. This can avoid overfitting to a specific domain of the review text by outputting BIO tags instead of the words.

Analisis Sentimen Pengungkapan Informasi Manajemen: Text Mining Berbasis Metode VADER

Financial statements present information related to the firm’s financial condition. However, there is various types of data are provided to help us in assessing and understanding the firm’s business condition. The most abundant firm-related data that are available are in the form of text. It can include annual reports, official websites, or even social media posts that may contain non-financial data. Nonfinancial information is also important to help the interpretation of financial information. The research aims to analyze the sentiment of management discussion and analysis included in the manufacturing company's annual report. The research sample is manufacturing companies listed on the Indonesia Stock Exchange (IDX) during the 2016-2020 period in a row. There are 102 companies or 510 research observations include in this research. This research uses a sentiment analysis technique based on a lexicon-based approach using the VADER method. Meanwhile, the sentiment analysis process will be assisted by the Orange Data Mining application. The results of the research show that positive disclosure is greater than negative sentiments. The pattern of sentiment for companies that are classified based on sector, company size (total assets and total sales), profitability (ROI), and liquidity (current ratio) shows relatively the same results. These results illustrate that company characteristics do not make a difference in the choice of word sentiment in manufacturing companies.

Lexicon-Based Sentiment Convolutional Neural Networks for Online Review Analysis

With the growing availability and popularity of sentiment-rich resources like blogs and online reviews, new opportunities and challenges have emerged regarding the identification, extraction, and organization of sentiments from user-generated documents or sentences. Recently, many studies have exploited lexicon-based methods or supervised learning algorithms to conduct sentiment analysis tasks separately; however, the former approaches ignore contextual information of sentences and the latter ones do not take sentiment information embedded in sentiment words into consideration. To tackle these limitations, we propose a new model named Sentiment Convolutional Neural Network (SentiCNN) to analyze the sentiments of sentences with both contextual and sentiment information of sentiment words, in which, contextual information is captured from word embeddings and sentiment information is identified using existing lexicons. We incorporate a Highway Network into our model to adaptively combine sentiment and contextual information from sentences by strengthening the connection between features of both sentences and their sentiment words. Furthermore, we propose three lexicon-based attention mechanisms (LBAMs) for our SentiCNN model to find the most important indicators of sentiments and make predictions more effectively. Experiments over two well-known datasets indicate that sentiment words, the Highway Network, and LBAMs contribute to sentiment analysis.

An Integrated Single Framework for Text, Image and Voice for Sentiment Mining of Social Media Posts

The wide spread pandemic COVID-19 has propelled the entire world to rely on social media interaction digitally. Social media is thus a platform to express numerous kinds of direct and indirect sentiments by human beings. Psychologically, a person tends to share his/her feelings in terms of sentiments more openly over the social media. These sentiments, when intense may polarize oneself to commit severe mis-deeds. Here arises the role of the researchers to perform a real time identification of sentiments and classify them so that a prospective mishap can be averted. In this work, an integrated framework is proposed that does an early recognition of sentiments over social media in the digital domain. Along with sentiment categorization, another module has been integrated to the framework to perform a post-predictive analysis of the same. The proposed integrated framework involves combination of two distinct mechanisms. First, the proposed work channelizes the input data in line with its characteristics text, image, and voice. The text input is directly fed to our proposed ‘Lexicon based LSTM with sentiment word mapping’ mechanism. From the input image, both text and semantics are extracted through two different blocks. One block converts image-to-text and redirects the output to the above proposed model. We proposed a new generative model (GM) to extract the semantics of the image and the second block utilizes our generative model and redirects the outcome straight to the final output buffer of the framework. The voice-to-text module has been used for transforming voice input data to text data which is redirected to our proposed Lexicon based LSTM for further processing. A comparison of the proposed work has been made with state-of-the-art techniques. Our results indicate that the overall rate of accuracy of this framework is superior to the existing methods.

NFT 기반 디지털 작품 인식 현황 연구

Recently, NFT, an irreplaceable token called convergence art that combines digital technology and art, is attracting attention in the field of design and art. The purpose of this study is to analyze NFT technology-based digital works that are rapidly growing along with rapid demand for IT technology through issues of the times, the development of cryptocurrency blockchain technology, and the trend change in the art market through text mining. As a method of research, text mining is conducted by setting the last year as a collection period from March 2021 to February 2022, when NFT grew the most rapidly using TextStom of Big Data tools. As a result, first, it was confirmed that works, sales, metaverse, and media, which are major controversial keywords in the NFT field reviewed in previous studies, are the main keywords that provide the foundation of potential means in planning NFT technology-based digital works. Second, NFT's "coin", "sales", "game", "metaverse", and "work" keywords showed that NFT art was provided in the form of a metaverse, or was considered to be the value of content creation in the form of a game or brand character. Third, it can be seen that it is considered as a new platform closely related to NFT digital work creators, such as guaranteeing intellectual property rights in virtual exhibitions related to copyrights in the nature of blockchain and securing accessibility to digital works. Fourth, although it was a small proportion in word sentiment analysis, it was found that NFT could cause financial losses in terms of negativity, and art or profits as investment products had a bubble in market prices, so it was necessary to be cautious. Starting with the analysis map proposed in this study, it is hoped to be helpful as current status data that can be referred to when constructing digital works based on NFT technology.

Does prospectus AE affect IPO underpricing? A content analysis of the Chinese stock market

Using content analysis, we study the effects of prospectus AE (referred as Form S-1 in U.S.) on the IPO underpricing. We measure the content of prospectus AE by IPO word lists, which we compiled on the basis of the Loughran-McDonald Sentiment Word Lists and the Chinese stock market. The results show that number of words in the prospectus AE is negatively related to underpricing while the percentage of negative and uncertain words in the prospectus AE have significantly positive impacts on underpricing. We also find that IPOs with more words or high levels of negative (uncertain) text in the prospectus AE are more likely to underperform in the future. In further analysis, we find that the effect of prospectus AE on underpricing is more prominent when investors have better ability of information processing, in lower market sentiments or the contents of prospectus AE are more optimistic and definite.

Brand Potential User Identification Algorithm Based on Sentiment Analysis.

This paper firstly compares the current research status of text sentiment analysis and potential customer identification, and introduces the process of building sentiment dictionaries and feature selection, feature screening, and common classification algorithms in text analysis. Secondly, around the most used tool for sentiment analysis, sentiment dictionary, the sentiment polarity discriminative rules of sentiment words are studied. In response to the shortcomings of using a single recognition algorithm in the current process of building sentiment dictionaries, an improved integration rule is designed and an automatic construction method for domain sentiment dictionaries in the social media environment is proposed. Then, this paper analyzes the sentiment topic information existing in user-generated content and adds the domain sentiment lexicon to the joint sentiment topic model as a posteriori information to extract the sentiment topic features, based on which the feature engineering study of potential customer identification is conducted and the feature set is constructed. In addition, a sample resampling method and a diverse integration framework for unbalanced data are designed to work together for the prospect identification task under data skewing in response to the category imbalance in real data. Finally, an experimental study is conducted using a social media text corpus to validate the proposed method in this paper. The proposed domain sentiment lexicon construction method and the joint domain sentiment topic-based lead identification method show good performance in different control group experiments. This paper provides an in-depth study on the construction of domain sentiment lexicon and imbalance classification in theory and provides solutions for companies to discover potential customers in practice, which has certain theoretical significance and practical value.

Does attention mechanism possess the feature of human reading? A perspective of sentiment classification task

PurposeTo understand the meaning of a sentence, humans can focus on important words in the sentence, which reflects our eyes staying on each word in different gaze time or times. Thus, some studies utilize eye-tracking values to optimize the attention mechanism in deep learning models. But these studies lack to explain the rationality of this approach. Whether the attention mechanism possesses this feature of human reading needs to be explored.Design/methodology/approachThe authors conducted experiments on a sentiment classification task. Firstly, they obtained eye-tracking values from two open-source eye-tracking corpora to describe the feature of human reading. Then, the machine attention values of each sentence were learned from a sentiment classification model. Finally, a comparison was conducted to analyze machine attention values and eye-tracking values.FindingsThrough experiments, the authors found the attention mechanism can focus on important words, such as adjectives, adverbs and sentiment words, which are valuable for judging the sentiment of sentences on the sentiment classification task. It possesses the feature of human reading, focusing on important words in sentences when reading. Due to the insufficient learning of the attention mechanism, some words are wrongly focused. The eye-tracking values can help the attention mechanism correct this error and improve the model performance.Originality/valueOur research not only provides a reasonable explanation for the study of using eye-tracking values to optimize the attention mechanism but also provides new inspiration for the interpretability of attention mechanism.

Sentiment analysis on consumers’ opinions – evaluating online retailers through analyzing sentiment for face masks during COVID-19 pandemic

ABSTRACT Customer opinions on a product or retailer can be assessed through analyzing their textual reviews submitted to online communities. While the attitudinal information of a text post can be gauged by studying the sentiment words used, a method has to be sought for more effectively evaluating the sentiment strengths, because different writers may have varied interpretations on a word’s sentiment. The objective of this paper is to develop a new integrated approach by adopting Sentiment Analysis and Evidential Reasoning for the online retailers’ timely evaluation of the public sentiments toward different product alternatives. The finding of the study has established the benefit of efficiently evaluating the customers’ feedback toward products and monitoring the changes of customer preferences on product attributes over time. The results obtained from the proposed approach are superiority to other decision-making tools.

Sentiment analysis of Hinglish reviews using hybrid approaches

People are using social media to communicate their views and opinions on several topics, and it is becoming more popular as a medium of communication. Nowadays people are curious to hear about a film review before they watch it for a few days. A summary of all reviews for a film can help users make their decision by not wasting time reading all reviews. Several websites can have summery because of the rise of social media.As a result, the words sentiment analysis and text analysis developed their paths to becoming important computational linguistics and text analysis components. Opinions are articulated in India in recent years, using multi-lingual terms. In the field of sentiment analysis, this has become a new challenge. Machine learning methods, such as neural networks, have proven effective in this task; however, there is space for advancing to networks with greater accuracy.In this investigation,U. S. English, Hindi dialects, and datasets like Twitter sentiment corpus, Amazon Product Reviews, IMDB Movie reviews, and Several public opinion datasets have been used in this study. The proposed network may achieve the greatest known accuracy of 92.25 percent. As an outcome, the success of the suggested network can be duplicated in various fields.