Harmonizing Perceptions: Exploring Alignment of Numerical and Textual Evaluation Through Fine-Grained Sentiment Analysis

The massive social media data bring timely, multi-dimensional and rich information. Recently, many researchers have worked on event summarization with crowdsourced social media data. While existing works mostly focus on text-based summary, they only summarize representative microblogs. Public sentiment for the event is also valuable; however, this is not explored in microblogging event summary. In this paper, we propose SentiStory, which is a multi-grained sentiment analysis and event summarization system that summarizes event from two levels: coarse-grained and fine-grained sentiment analysis. In coarse-grained analysis, it discovers microblogs which are important in sentiment, while in fine-grained analysis, it detects significant change of sentiment in the event and identifies which microblog causes the change. Specifically, the proposed system comprises two modules: (1) the microblog preprocessing module firstly reduces redundant information and extracts useful information from the microblog database, and then, it separates different aspects of the event and clusters the same aspect together in a clue. (2) The multi-grained sentiment analysis model analyzes microblogs from two levels: coarse-grained and fine-grained. We perform detailed experimental study on real dataset collected from Sina Weibo, and the results demonstrate the effectiveness of our approach.

Aspect-based sentiment analysis as fine-grained sentiment analysis problem, mainly aims to clarify the sentiment of different aspect. Previous fine-grained sentiment analysis methods usually used Recurrent Neural Networks as feature extractors, and achieved good results in sentiment analysis. However, they were limited to the sequential processing of sequence by RNN. Its training and operation efficiency are relatively low and time consuming. Aiming at this problem, combining the multi-head attention mechanism in Transformer, fusion model of convolution neural network and hierarchical attention coding network is proposed, and convolution neural network is used as feature encoder to improve the efficiency of model training. At the same time, multi-head attention is used to calculate the degree of relevance between aspect words and context, thus capturing emotional elements corresponding to entity words. Experiments on the SemEval2014 dataset have improved the training and inference efficiency of aspect-based sentiment analysis.

Purpose This research employs innovative deep learning techniques to perform sentiment analysis on airline customer feedback, systematically investigating service quality dimensions and customer satisfaction determinants through the theoretical lens of the SERVQUAL framework. Design/methodology/approach We analyzed airline reviews from TripAdvisor and Skytrax from July 2014 to July 2023 using SERVQUAL theory. Coarse-grained clustering revealed themes in tangibles and reliability, while fine-grained sentiment analysis, using our FusionBERT model, assessed responsiveness, assurance and empathy for deeper service quality insights. Findings Our approach outperformed baselines, offering a nuanced analysis of airline customer satisfaction using the SERVQUAL theory. Coarse-grained sentiment analysis assesses tangibles and reliability (facilities, service consistency), while fine-grained analysis evaluates responsiveness, assurance, and empathy (staff promptness, professionalism and care). This dual-layered method enables comprehensive service quality evaluation, helping airlines identify strengths and improvement areas. Originality/value This research provides actionable management solutions and recommendations for airline managers.

Under the goal of carbon peak and carbon neutrality, developing battery electric vehicles (BEVs) is an important way to reduce carbon emissions in the transportation sector. To popularize BEVs as soon as possible, it is necessary to study selection strategies for BEVs from the perspective of consumers. Therefore, the Latent Dirichlet Allocation (LDA) model based on fine-grained sentiment analysis is combined with the multi-criteria decision-making (MCDM) model to assess ten types of BEV alternatives. Fine-grained sentiment analysis is applied to find the vehicle attributes that consumers care about the most based on the word-of-mouth data. The LDA model is suggested to divide topics and construct the indicator system. The MCDM model is used to rank vehicles and put forward the corresponding optimization path to increase consumer purchases of BEVs in China. The results show that (a) via the LDA model based on fine-grained sentiment analysis, attributes that consumers care most about are divided into five topics: dynamics, technology, safety, comfort, and cost; (b) based on the DEMATEL technique, the dimensions in the order of importance are as follows: safety, technology, dynamics, comfort, and cost; (c) the price is the most important criteria that affect customers’ satisfaction by the DANP model; and (d) based on the VIKOR model, the selection strategies present that Aion S is highlighted as the best choice, and the optimization path is discussed to promote the performance of BEVs to increase customers’ satisfaction. The findings can provide a reference for improving the sustainable development of the automobile industry in China. The proposed framework serves as the basis for further discussion of BEVs.

Deciphering user purchase preferences, their likes and dislikes is a very tricky task even for humans, making its automation a very complex job. This research work augments heuristic-driven user interest profiling with reviewer credibility analysis and fine-grained feature sentiment analysis to devise a robust recommendation methodology. The proposed credibility, interest and sentiment enhanced recommendation (CISER) model has five modules namely candidate feature extraction, reviewer credibility analysis, user interest mining, candidate feature sentiment assignment and recommendation module. Review corpus is given as an input to the CISER model. Candidate feature extraction module uses context and sentiment confidence to extract features of importance. To make our model robust to fake and unworthy reviews and reviewers, reviewer credibility analysis proffers an approach of associating expertise, trust and influence scores with reviewers to weigh their opinion according to their credibility. The user interest mining module uses aesthetics of review writing as heuristics for interest-pattern mining. The candidate feature sentiment assignment module scores candidate features present in review based on their fastText sentiment polarity. Finally, the recommendation module uses credibility weighted sentiment scoring of user preferred features for purchase recommendations. The proposed recommendation methodology harnesses not only numeric ratings, but also sentiment expressions associated with features, customer preference profile and reviewer credibility for quantitative analysis of various alternative products. The mean average precision (MAP@1) for CISER is 93% and MAP@3 is 49%, which is better than current state-of-the-art systems.

The emergence of various e-commerce sites has led to an increase in review sites for various services and products. People nowadays easily get information about products and services that will be used through reviews. Here sentiment analysis plays an important role in classifying the polarity of product reviews. However, with a large number of reviews, a sentiment analysis that only gives overall polarity is not sufficient. This will make it difficult to find the reviews of certain aspects (features) of the product. Aspect-based sentiment analysis as fine-grained sentiment analysis is able to provide specific polarity for each aspect contained in a sentence. Various kinds of development methods have been carried out to provide accurate results in aspect-based sentiment analysis. This paper will discuss the various deep learning methods that have been carried out and provide the possibility of research that can be carried out from Aspect-Based Sentiment Analysis.

In the present age of rapid digital and technological progress, individuals spend a significant portion of their day connected to the Internet, actively expressing their thoughts and emotions on various online platforms. The analysis of these comments is important for detecting fundamental sentiments that lead to informed decisions across numerous sectors. Sentiment analysis (SA) classifies textual or spoken expressions as positive, negative, or neutral. Although several machine learning and NLP models are available for SA, accurately extracting and categorizing opinions remains challenging. This study compared seven state-of-the-art models-LSTM, BiLSTM, CNN, random forest, logistic regression, support vector machine (SVM), and naïve Bayeswith the BERT model to achieve precise sentiment classification. TF-IDF vectorization was performed before the SA. Using 11,323 annotated sentences from three domains, the models were assessed based on the F1- score, recall, precision, and accuracy. Each sentence was labeled to reflect either a positive or negative sentiment. The experimental results show that the BERT model performs best, achieving an accuracy of 99 % for Games data, 94 % for the comprehensive data, 90 % for the social networking data, and 97% for the productivity data. For all the data types, BERT outperformed all the state-of-the-art models considered for comparison. The findings offer practical insights for the mobile app industry by enhancing the understanding of user opinions through SA. This study contributes to real-world applications by guiding the effective selection of mobile apps based on fine-grained sentiment analysis.

Developing online educational platforms necessitates the incorporation of new intelligent procedures in order to improve long-term student experience. Presently, e-learning recommender systems rely on deep learning methods to recommend appropriate e-learning materials to the students based on their learner profiles. Fine-grained sentiment analysis (FSA) can be leveraged to enrich the recommender system. User-posted reviews and rating data are vital in accurately directing the student to the appropriate e-learning resources based on posted comments by comparable learners. In this work, a new e-learning recommendation system is proposed based on individualization and FSA. A hybrid framework is provided by integrating alternating least square (ALS) based collaborative filtering (CF) with FSA to generate an effective e-content recommendation named HCFSAR. ALS attempts to capture the learner’s latent factors based on their selections of interest to build the learner profile. Three FSA models based on attention mechanisms and bidirectional long short-term memory (bi-LSTM) are suggested and used to train twelve models in order to predict new ratings from learner-posted book reviews based on the extracted learner profile. HCFSAR used multiplication word embeddings for stronger corpus representation that were trained on a dataset generated for an educational context and showed a better accuracy of 93.39% for the best model entitled MHAM based ABHR-2 with multiplication (MHAAM), which performed better than other models. A tailored dataset that has been created by scraping reviews of different e-learning resources is leveraged to train different proposed models and validate against public datasets.

This paper describes the approach we used for SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs. We use three types of word embeddings in our algorithm: word embeddings learned from 200 million tweets, sentiment-specific word embeddings learned from 10 million tweets using distance supervision, and word embeddings learned from 20 million StockTwits messages. In our approach, we also take the left and right context of the target company into consideration when generating polarity prediction features. All the features generated from different word embeddings and contexts are integrated together to train our algorithm

Aspect-based sentiment analysis is an important task in fine-grained sentiment analysis, which aims to infer the sentiment towards a given aspect. Previous studies have shown notable success when sufficient labeled training data is available. However, annotating adequate data is labor-intensive, which sets substantial barriers for generalizing the sentiment predictor to the new domain. Two main challenges exist in cross-domain aspect-based sentiment analysis. One challenge is acquiring the domain-invariant knowledge; the other challenge is mining the syntactic-related words towards the aspect-term. In this article, we propose a transformer-based semantic-primary knowledge transferring network (TSPKT) for cross-domain aspect-term sentiment analysis, which utilizes semantic-primary knowledge as a bridge to enable knowledge transfer across domains. Specifically, we first build an S-Graph from external semantic lexicons, and extract the semantic-primary knowledge from the S-Graph. Second, AoaGraphormer is proposed to learn the syntactically relevant words towards the aspect-term. Third, we extend the standard biLSTM classifier to fully integrate the semantic-primary knowledge by adding a novel knowledge-aware memory unit (KAMU) to the biLSTM cell. Extensive experiments on six cross-domain setups demonstrate the superiority of TSPKT against the state-of-the-art baseline methods.

Aspect-based sentiment analysis is a fine-grained sentiment analysis that focuses on the sentiment polarity of different aspects of text, and most current research methods use a combination of dependent syntactic analysis and graphical neural networks. In this paper, a graph attention network aspect-based sentiment analysis model based on the weighting of dependencies (WGAT) is designed to address the problem in that traditional models do not sufficiently analyse the types of syntactic dependencies; in the proposed model, graph attention networks can be weighted and averaged according to the importance of different nodes when aggregating information. The model first transforms the input text into a low-dimensional word vector through pretraining, while generating a dependency syntax graph by analysing the dependency syntax of the input text and constructing a dependency weighted adjacency matrix according to the importance of different dependencies in the graph. The word vector and the dependency weighted adjacency matrix are then fed into a graph attention network for feature extraction, and sentiment polarity is predicted through the classification layer. The model can focus on syntactic dependencies that are more important for sentiment classification during training, and the results of the comparison experiments on the Semeval-2014 laptop and restaurant datasets and the ACL-14 Twitter social comment dataset show that the WGAT model has significantly improved accuracy and F1 values compared to other baseline models, validating its effectiveness in aspect-level sentiment analysis tasks.

Intent analysis and classification are performed to identify the expressions of intent in the given text. In this paper, the dataset is classified into emotion classifications by utilizing machine learning model SVM, Bipolar classification, Fine Grained Analysis, and Sarcasm detection, with Naïve Bayes and Random Forest techniques of deep learning, including Long Short-Term Memory to perform intention analysis on social media data. Then Fine-grained or Multi-Class Sentiment analysis is used for further classification of the five classes, viz. negative, strong negative, neutral, positive, and strong positive, which detects the sarcastic reviews in the movie dataset. The emotional intention behind the review comments is classified as happiness, rage, sadness, joy, anger, and disgust by using SVM. The reviews are analyzed and calculated based on their subjectivity and context level similarity using Related Relaxed Word Mover Distance (RRWMD) semantic similarity measure. With the advantage of the RRWMD algorithm, the reviews from the context containing deviated or irrelevant contents were removed before being applied to the classification algorithms, thereby reducing the execution time, which obtains a 3% improvement in accuracy. The disadvantage of the RRWMD algorithm is only one deep learning algorithm is compared. From the observed accuracy scores and classification reports, the LSTM has provided higher accuracy, despite the long execution time. The Naïve Bayes model has produced lower accuracy than the neural network model but was efficient, taking less time to fit and classify. The results from various experiments have proven that the semantic similarity measure provides more accurate results than the state-of-the-art model.

This study aimed at examining the perception of faculty members towards the use of student evaluations of faculty members as a basis to assess teaching effectiveness and for decision-making for annual faculty review. The study was conducted with the participation of 62 full-time members at Catholic University and Sunyani Technical University. Quantitative and qualitative data were collected through questionnaires and interview guides. The main findings revealed that faculty members positively perceive the current Students’ Evaluation of Faculty (SEF) instrument as effective in evaluating teaching effectiveness but do not perceive the use of the current instrument as effective in decision-making for annual review. The study also revealed that there was no association between the objectives for the adoption and usage of the Students’ Evaluation of Faculty (SEF) instrument for private and public universities. Finally, the findings show that faculty are challenged both administratively and humanly in their endeavour to effectively implement SEF. Based on the findings, it was concluded that the evaluation method and instrument needed to be revised further to include a multidimensional procedure. This multifaceted method should produce independent instruments for annual faculty reviews and teaching improvement. Keywords: Faculty evaluation, perception, teaching effectiveness, decision-making.

Fine-grained sentiment analysis on the Web has received much attention in recent years. In this paper we suggest an approach to Aspect-Based Sentiment Analysis that incorporates structural information of reviews by employing Rhetorical Structure Theory. First, a novel way of determining the context of an aspect is presented, after which a full path analysis is performed on the found context tree to determine the aspect sentiment. Comparing the proposed method to a baseline model, which does not use the discourse structure of the text and solely relies on a sentiment lexicon to assign sentiments, we find that the proposed method consistently outperforms the baseline on three different datasets.

The traditional family responsibility system faces challenges as China undergoes rapid demographic shifts with an increasingly older population. Recognizing the potential of market-driven senior care, Continuing Care Retirement Communities (CCRCs) have emerged as a significant alternative. However, cultural stigmas and concerns about the quality, services, and health of older adults in these facilities raise questions over their broad acceptance. This study examines public sentiment toward CCRCs through sentiment analysis of 1,027,295 pre-processed Weibo posts. Utilizing Natural Language Processing (NLP) combined with fine-grained sentiment analysis and the Term Frequency-Inverse Document Frequency algorithm, the attitudes and emotions reflected in each data point are analyzed, identifying key contributing factors, and exploring the underlying reasons. The results reveal a predominantly positive sentiment toward CCRCs, emphasizing factors such as the living environment and government involvement. However, areas of concern, such as potential fraud and health and safety issues, remain. These findings both shed light on the public's acceptance or resistance to CCRCs for stakeholders and highlight the potential of social media analysis in shaping older people's care perceptions in today's China.