Text Sentiment Research Articles

Enhancing the interpretability of sentiment analysis with signatures

Sentiment analysis is a significant research area within the field of natural language processing, with applications in various social contexts. Recent advances in related research have led to notable improvements in accuracy, yet existing text sentiment analysis models continue to face challenges in terms of interpretability. This paper proposes a text sentiment classification method integrating BERT and BiLstm, comprising three stages: training, signature generation, and testing. Our primary contribution lies in the signature generation and testing stages. In the training stage, BERT is employed to generate word embeddings, while BiLstm is utilized for learning to construct the text sentiment classification model. In the signature generation stage, the training and validation sets are passed through the aforementioned classification model, the feature vectors are extracted following their passage through the fully-connected layer, and the feature vectors are utilized to generate the signature generation stage. The signature is then stored in the database. In the Testing Stage, the aforementioned operation is repeated on the test set in order to obtain the signatures. The signatures are then matched in the constructed signature database in order to obtain the final classification results. The matching degree is subsequently used as a threshold in order to be employed for subsequent evaluation of the results. The method enhances the interpretability of the classification task by matching through signatures. Two datasets, SST-2 and IMDB, were subjected to experimentation. Four quantitative metrics were employed for the evaluation of the results: accuracy, recall, precision, and F1-score. With a threshold of 0.99, the models optimal accuracy, recall, precision, and F1-score results reached 0.896, 1, 0.896, 0.945 and 0.847, 0.999, 0.848, 0.917, respectively.

Intelligent Text Sentiment Analysis of Emotional Beauty in the English Translation: Based on TB-LSTM Algorithm

In the existing deep learning sentiment analysis methods, text is directly processed into a sequence of words, which often ignores the important components of the text and the topic of the text. To solve these problems, the topic of text is extracted through Latent Dirichlet Allocation model, and then a method for integrating topic information into neural networks is proposed. Furthermore, an emotion classification model that integrates the topic information is constructed. An attention mechanism is used at the lexical level and the sentence level, paying attention to the more and less important content separately when building the document presentation. The results show that the proposed model has excellent performance in all four kinds of data, among which the accuracy rate, recall rate and F1-score of CCF-BDC are the best, reaching 0.63, 0.72 and 0.7 respectively. The translation accuracy also reaches 0.85. These results show that the model proposed in this study has better performance in sentiment analysis. These research results will help improve the user's reading experience and make machine translation better meet the needs of human language expression.

Experience in psychological counseling supported by artificial intelligence technology.

In recent years, artificial intelligence (AI) technology has been continuously advancing and finding extensive applications, with one of its core technologies, machine learning, being increasingly utilized in the field of healthcare. This research aims to explore the role of Artificial Intelligence (AI) technology in psychological counseling and utilize machine learning algorithms to predict counseling outcomes. Firstly, by employing natural language processing techniques to analyze user conversations with AI chatbots, researchers can gain insights into the psychological states and needs of users during the counseling process. This involves detailed analysis using text analysis, sentiment analysis, and other relevant techniques. Subsequently, machine learning algorithms are used to establish predictive models that forecast counseling outcomes and user satisfaction based on data such as user language, emotions, and behavior. These predictive results can assist counselors or AI chatbots in adjusting counseling strategies, thereby enhancing counseling effectiveness and user experience. Additionally, this study explores the potential and prospects of AI technology in the field of psychological counseling. The research findings indicate that the designed machine learning models achieve an accuracy rate of approximately 89% in analyzing psychological conditions. This demonstrates significant innovation and breakthroughs in AI technology. Consequently, AI technology will gradually become a highly important tool and method in the field of psychological counseling. In the future, AI chatbots will become more intelligent and personalized, providing users with precise, efficient, and convenient psychological counseling services. The results of this research provide valuable technical insights for further improving AI-supported psychological counseling, contributing positively to the application and development of AI technology.

Construction of the tourist sentiment dictionary for hotels to mining tourist demands: Based on Macao’s hotel reviews from Agoda

<p><span lang="AZ-LATIN">Tourist hotels (or tourist accommodations) are located near tourist attractions, primarily serving tourists. In recent years, with the gradual improvement of people’s living standards around the globe, tourists’ demands and standards for tourist hotel construction have been rising accordingly. In the context of technologization and informatization, various hotel booking platforms (Agoda, Booking, Trip, etc.) cover a large amount of review data in evaluating systems to reflect tourists’ demands. Meanwhile, identifying demand-oriented reviews and extracting core consumer demands from them is crucial for optimizing hotel services and enhancing tourist satisfaction. Therefore, this study explores the demands of tourists in tourist hotels from the perspective of text sentiment analysis and takes Macao, a famous tourist destination, as an example, based on reviews of tourist hotels on the Agoda site platform. Specifics are as follows: (1) Based on pointwise mutual information (PMI) and information entropy (IE), it realizes the identification of sentiment words in the field of tourist hotels and constructs a sentiment dictionary to address the problem of poor relevance between word segmentation results; (2) It summarizes the five types of reviews containing tourist demands (positive, negative, suggestion, demand, and comparison) and their characteristics to solve the ambiguity of texts and further accurately reveal the main demands of tourists; (3) It classifies tourist demands and group similar tourist demands into the same categories to address the problem of multiple expressions for the same demand. The present study provides empirical experiences from Macao’s hotels and contributes to the literature on text sentiment analysis in tourist hotels. Furthermore, the study results could enhance the mining accuracy and provide a detailed summarization of consumer demands and directions for the sustainable optimization improvement of tourist services.</span></p>

Sentiment analysis of tourism review text combined with bert-bilstm and attention mechanism

Word2vec is often used in text sentiment analysis to generate word vector, which maps the same word into the same vector. Although Word2vec plays a very good effect in the initial model training task, it still cannot solve the problems of polysemy and new use of old words, which leads to inaccurate extracted features and affects the final classification results. In this paper, BERT model was used to vectorize the review text of tourist attractions, and fusion attention mechanism and long and short-term memory model were used to extract the emotional features of the text for classification at the feature extraction layer. The emotional accuracy of the model proposed in this paper reached 95.79% in the review text of tourist attractions.

Deciphering Tourism’s Role in Antarctica’s Geosocial Concerns through Data Mining Techniques

This study explores the changing dynamics of tourism in Antarctica, focusing on the impact of digitalisation and User-Generated Content on platforms like Tripadvisor. It aims to understand how online reviews influence perceptions and decisions to visit Antarctica, a region known for its pristine environment and status as ‘the last frontier’. Utilising Environmental Perception and Behaviour Geography (EPBG) principles, this research conducts a quantitative analysis of reviews from potential and current travellers. Through text mining, topic modelling, sentiment analysis, and Natural Language Processing (NLP), it investigates the emotional and perceptual discourse surrounding Antarctic tourism and its alignment with Agenda 2030 and Sustainable Development Goals. The findings reveal a detailed narrative of sustainability challenges and the emotional geography related to tourism in Antarctica, highlighting emotions such as happiness, anger, surprise, fear, disgust, and sadness among visitors. This study uncovers differences in perception based on visitors’ backgrounds, noting that individuals from nature-focused cities display strong environmental concerns, whereas those from advanced urban centres show a more positive attitude. This research contributes to the understanding of EPBG, text mining, and NLP, offering insights into sustainable tourism practices in Antarctica.

Legislating landlords: Private interests, issue emphasis, and policy positions

AbstractDo private interests predict politicians' rhetoric? Focusing on housing policy, we compare issue emphasis and positions of landlord politicians and politicians who do not own multiple properties. Ireland provides a unique opportunity to study legislating landlords' behavior as housing has become one of the most important political issues. We construct a novel dataset of politicians' homeownership status between 2013 and 2022, a period characterized by rising rent and property prices. We fine‐tune a transformer‐based machine learning model and apply text scaling and sentiment analysis to identify issue salience and positions on housing in over 870,000 tweets and parliamentary questions. Contrary to our expectations, landlord politicians do not avoid the topic of housing, nor do they take different positions. We also find that government status does not influence this relationship. The results imply that private financial interests do not influence rhetoric on housing policy.

Identification of User Requirements and their Influencing Factors Based on Online Reviews and Operational Data

It is crucial for enterprises to clearly identify user needs during the process of formulating product design improvement plans. Therefore, it is essential to comprehensively and accurately identify user needs, explore the reasons behind the emergence of these needs, and incorporate user opinions into the process of product design improvement. A method is proposed to comprehensively and accurately capture user requirements and address the challenge of identifying the underlying causes of user requirements. This method utilizes online comments and operational data to identify user requirements and their influencing factors. First, text sentiment analysis techniques are employed to quantify the importance and performance values of product feature topics. Second, we construct a quadrant model to identify product features requiring improvement, and the original negative comments related to these features are traced. However, the quadrant model alone is insufficient to reflect specific product issues that users are concerned about. Therefore, a functional structure model based on product issues is designed to filter and identify factors that influence user requirements using operational data. Finally, a Bayesian network inference approach is utilized to identify the key influencing factors on user requirements, enabling analysis of the causes behind user requirements and the proposal of product design improvement strategies. The feasibility and effectiveness of the proposed method are validated through experiments conducted on heavy-duty truck data. By analyzing the original negative comments related to the power characteristics, specific user demands regarding the insufficient power of the product were identified, such as “obviously insufficient power when climbing slopes” and other issues. Based on the vehicle power system functional structure model, combined with expert knowledge and operational data, factors related to the state of parts and user behavior that may affect “insufficient vehicle power” were identified. Based on the analysis results, suggestions were made to improve the engine intake air temperature control strategy and to enhance vehicle performance by promoting correct user behavior through informational campaigns.

Deep learning, textual sentiment, and financial market

Deep learning, textual sentiment, and financial market

Comparing patient education tools for chronic pain medications: Artificial intelligence chatbot versus traditional patient information leaflets.

Artificial intelligence (AI) chatbots like Conversational Generative Pre-trained Transformer (ChatGPT) have recently created much buzz, especially regarding patient education. Such informed patients understand and adhere to the management and get involved in shared decision making. The accuracy and understandability of the generated educational material are prime concerns. Thus, we compared ChatGPT with traditional patient information leaflets (PILs) about chronic pain medications. Patients' frequently asked questions were generated from PILs available on the official websites of the British Pain Society (BPS) and the Faculty of Pain Medicine. Eight blinded annexures were prepared for evaluation, consisting of traditional PILs from the BPS and AI-generated patient information materials structured similar to PILs by ChatGPT. The authors performed a comparative analysis to assess materials' readability, emotional tone, accuracy, actionability, and understandability. Readability was measured using Flesch Reading Ease (FRE), Gunning Fog Index (GFI), and Flesch-Kincaid Grade Level (FKGL). Sentiment analysis determined emotional tone. An expert panel evaluated accuracy and completeness. Actionability and understandability were assessed with the Patient Education Materials Assessment Tool. Traditional PILs generally exhibited higher readability (P values < 0.05), with [mean (standard deviation)] FRE [62.25 (1.6) versus 48 (3.7)], GFI [11.85 (0.9) versus 13.65 (0.7)], and FKGL [8.33 (0.5) versus 10.23 (0.5)] but varied emotional tones, often negative, compared to more positive sentiments in ChatGPT-generated texts. Accuracy and completeness did not significantly differ between the two. Actionability and understandability scores were comparable. While AI chatbots offer efficient information delivery, ensuring accuracy and readability, patient-centeredness remains crucial. It is imperative to balance innovation with evidence-based practice.

What makes UGC more popular on social media platforms? Insights from information adoption theory

ABSTRACT It is becoming an important marketing strategy for enterprises to utilise user-generated content (UGC) to build brands and promote products on social media platforms, but little is known about the influence mechanism of the popularity of UGC related to brands and products. Drawing upon information adoption theory, this study developed a conceptual model to explain what makes UGC more popular in social media context. Using a dataset of 16,974 records related to the UGC about products from Xiaohongshu app, the most popular user experience sharing platform in China, the empirical study indicates that factors extracted from information quality and source credibility have significant effects on UGC popularity. Interestingly, text length and strength of text sentiment have different effects moderated by product types. In addition, media richness may lead to information redundancy and weaken the effect of text length. The findings offer an insightful understanding into the mechanism of utilising UGC in product marketing on social media platforms.

Stakeholders’ Satisfaction on Institutional Assessment: A Proposal for Unified Feedback Management System with Text Analytics and Sentiment Analysis

Stakeholders’ Satisfaction on Institutional Assessment: A Proposal for Unified Feedback Management System with Text Analytics and Sentiment Analysis

Patient-patient interactions visualization for drug side effects in patients’ reviews

This paper describes the patient-patient interactions (PPIs) graph extraction framework from patient’s review transcripts. The concept is to visualise patients as nodes and interactions representing links. Links are made based on review text similarity. Nodes are categorized as positive or negative according to the patient’s attitude toward a given drug. Attitudes are then utilized to categorize the links as supporting or opposing the use of a certain drug. If both patients share the same attitude: negative (severe side effect) or positive (moderate side effect), the relationship is considered supportive; if not, the link is considered opposed. Resulting graph represent a drug as a dispute between two factions arguing on related drug. The framework is explained and evaluated using a dataset included 3,763 patients’ reviews linked to 255 different drugs, -predictive-value (0.37). We argue that, this is caused by derogatory jargon that is an expected feature of patient’s review. The true-negative-recognition-rate is 0.70 and true-positive-recognition-rate is 0.54. Total-average-accuracy, which is independent of class priors, is 0.66. Results show that, it is possible to use text proximity measures and sentiment analysis to capture PPIs structure.

Emoji driven crypto assets market reactions

Abstract In the burgeoning realm of cryptocurrency, social media platforms like Twitter have become pivotal in influencing market trends and investor sentiments. In our study, we leverage GPT-4 and a fine-tuned transformer-based BERT model for a multimodal sentiment analysis, focusing on the impact of emoji sentiment on cryptocurrency markets. By translating emojis into quantifiable sentiment data, we correlate these insights with key market indicators such as BTC Price and the VCRIX index. Our architecture’s analysis of emoji sentiment demonstrated a distinct advantage over FinBERT’s pure text sentiment analysis in such predicting power. This approach may be fed into the development of trading strategies aimed at utilizing social media elements to identify and forecast market trends. Crucially, our findings suggest that strategies based on emoji sentiment can facilitate the avoidance of significant market downturns and contribute to the stabilization of returns. This research underscores the practical benefits of integrating advanced AI-driven analyzes into financial strategies, offering a nuanced perspective on the interaction between digital communication and market dynamics in an academic context.

Predicting multi-label emojis, emotions, and sentiments in code-mixed texts using an emojifying sentiments framework

In the era of social media, the use of emojis and code-mixed language has become essential in online communication. However, selecting the appropriate emoji that matches a particular sentiment or emotion in the code-mixed text can be difficult. This paper presents a novel task of predicting multiple emojis in English-Hindi code-mixed sentences and proposes a new dataset called SENTIMOJI, which extends the SemEval 2020 Task 9 SentiMix dataset. Our approach is based on exploiting the relationship between emotion, sentiment, and emojis to build an end-to-end framework. We replace the self-attention sublayers in the transformer encoder with simple linear transformations and use the RMS-layer norm instead of the normal layer norm. Moreover, we employ Gated Linear Unit and Fully Connected layers to predict emojis and identify the emotion and sentiment of a tweet. Our experimental results on the SENTIMOJI dataset demonstrate that the proposed multi-task framework outperforms the single-task framework. We also show that emojis are strongly linked to sentiment and emotion and that identifying sentiment and emotion can aid in accurately predicting the most suitable emoji. Our work contributes to the field of natural language processing and can help in the development of more effective tools for sentiment analysis and emotion recognition in code-mixed languages. The codes and data will be available at https://www.iitp.ac.in/~ai-nlp-ml/resources.html#SENTIMOJI to facilitate research.

The impact of the Seller's facial image on consumer purchase behavior in peer-to-peer accommodation platforms

Consumers' initial impressions of a seller's facial appearance play a crucial role in influencing purchase behavior. However, the underlying mechanisms by which a seller's facial appearance influences consumer purchase behavior remain unexplored. This study adopted a multimethod approach to explore these questions. First, drawing on the profile pictures of 1240 sellers from the Airbnb peer-to-peer accommodation platform, we investigated the impact of two principal facial attributes (facial attractiveness and facial expressions) on the formation of consumers' initial impressions and their subsequent influence on purchase decisions. Second, we explored the underlying mechanisms through two online experiments. Our findings indicate a U-shaped relationship between sellers' facial attractiveness and consumer purchase behavior. Additionally, we found that facial expressions such as happy, surprise, and sad, influence consumer purchase behavior positively in comparison to neutral expressions. Furthermore, our study demonstrates that the purchase price acts as a moderator of the U-shaped effect of facial attractiveness on consumer purchase behavior, while text sentiment intensity serves as a negative moderator of the impact of facial expressions of consumer purchase behavior. Finally, the study elucidates the influence mechanism of facial attractiveness and facial expressions on consumers' purchasing decisions. These insights provide novel perspectives on the role of sellers' facial attributes and offer valuable references for devising effective marketing strategies and optimizing platform operational services related to the design of seller facial imagery.

Identification and Classification of Bilingual Language using Natural Language Processing

This paper presents an efficient approach to language detection and sentiment analysis using Naive Bayes classifiers. The research involves training classifiers on a diverse dataset of text samples in multiple languages. Our method employs vectorization techniques and separate classifiers for language detection and sentiment analysis tasks. Experimental results demonstrate the effectiveness of the classifiers, with high accuracy in identifying the language and sentiment of input text. The approach showcases notable efficiency in processing language detection and sentiment analysis, addressing the growing need for automated text processing solutions. The findings have implications for various domains, including customer feedback analysis, market research, and opinion Mining

Desktop Assistant Based on NLP

Natural Language Processing (NLP) has emerged as a critical component of artificial intelligence, enabling machines to comprehend and interact with human language. This research paper explores the current state of the art in NLP, highlighting recent innovations, trends, and ongoing challenges. It delves into various applications of NLP, discusses the datasets and models that drive advancements, and examines the evaluation metrics used to assess NLP systems. Key innovations such as transformers, pre-trained language models, and transfer learning have revolutionized the field, leading to significant improvements in performance across a variety of tasks. Additionally, the paper addresses the growing emphasis on ethical AI and bias mitigation, as well as the integration of NLP with other AI technologies to create multimodal systems. Applications of NLP in text classification, sentiment analysis, machine translation, conversational agents, and information retrieval are thoroughly examined. The discussion extends to the critical role of benchmark datasets and pre-trained models in driving progress. Furthermore, the paper evaluates the effectiveness of various metrics used to measure the performance of NLP systems. Finally, the future prospects and potential research directions are considered, highlighting the ongoing efforts to push the boundaries of what NLP can achieve in an increasingly interconnected and data-driven world. Keywords Natural language processing . Natural language understanding . Natural language generation

A Brief Review on Preprocessing Text in Arabic Language Dataset: Techniques and Challenges

Text preprocessing plays an important role in natural language processing (NLP) tasks containing text classification, sentiment analysis, and machine translation. The preprocessing of Arabic text still presents unique challenges due to the language's rich morphology, complex grammar, and various character sets. This brief review studied various techniques utilized for preprocessing Arabic text data. This study discusses the challenges specific to Arabic text and current an overview of key preprocessing steps including normalization, tokenization, stemming, stop-word removal, and noise reduction. This survey analyzes preprocessing techniques on NLP tasks and focus on current research trends and future directions in Arabic text preprocessing.

Incorporating Multi-Source Market Sentiment and Price Data for Stock Price Prediction

The problem of stock price prediction has been a hot research issue. Stock price is influenced by various factors at the same time, and market sentiment is one of the most critical factors. Financial texts such as news and investor comments reflect investor sentiment in the stock market and influence market movements. Previous research models have struggled to accurately mine multiple sources of market sentiment information originating from the Internet and traditional sentiment analysis models are challenging to quantify and combine indicator data from market data and multi-source sentiment data. Therefore, we propose a BERT-LLA stock price prediction model incorporating multi-source market sentiment and technical analysis. In the sentiment analysis module, we propose a semantic similarity and sector heat-based model to screen for related sectors and use fine-tuned BERT models to calculate the text sentiment index, transforming the text data into sentiment index time series data. In the technical indicator calculation module, technical indicator time series are calculated using market data. Finally, in the prediction module, we combine the sentiment index time series and technical indicator time series and employ a two-layer LSTM network prediction model with an integrated attention mechanism to predict stock close price. Our experiment results show that the BERT-LLA model can accurately capture market sentiment and has a strong practicality and forecasting ability in analyzing market sentiment and stock price prediction.