Sentiment analysis method of consumer reviews based on multi-modal feature mining

Sentiment analysis of subjective texts in social media is beneficial to help people adjust and intervene in a negative mental state in time, which is significant to mental health care. At present, limited by the accuracy of word segmentation, sentiment analysis of subjective text has difficulties in dealing with context, sentence patterns, and word co-occurrence. This paper aims to propose an efficient method of semantic feature representation and sentiment analysis, thereby providing a basis for sentiment visualization and interactive applications. Based on Ernie-Tiny and BiGRU, this paper proposes a sentiment analysis model ET_s_BG+p to solve problems in analyzing Chinese subjective texts’ complex semantics, diverse sentence patterns, and shortness. The model inputs the semantic features obtained via Ernie-Tiny into BiGRU and then splices the output with the sentence vectors of Ernie-Tiny to form final text features and perform sentiment classification. Experiments are performed on a dataset integrating text comments from Weibo, takeaway, and e-commerce platforms. The results show that the model proposed in this paper performs best in most of the evaluation indicators compared with baseline models such as CNN, BiLSTM, and GRU. The experiments show that the accuracy of the model on the dataset built in this research is 84.30%, the precision is 83.95%, the recall rate is 88.35%, and the F1 value is 85.98%. At the same time, based on ET_s_BG+p, this paper develops a prototype visual display platform that integrates functions such as text input, sentiment analysis, and agent interaction, which can provide support for daily emotion monitoring and adjustment.

In the era of rapid digital transformation, social networks generate huge amounts of textual data every day, making sentiment analysis an essential tool for understanding public opinion. This study focuses on the application of probabilistic and statistical methods to sentiment analysis in social networks, highlighting their effectiveness in dealing with uncertainty and modeling the distribution of emotions. The main objective is to evaluate the role of Nave Bayesian (NB), Hidden Markov models (HMMs), and Bayesian networks in emotion classification, emotion propagation, and dynamic emotion tracking. Through literature review and comparative analysis, this study examines the existing research, computational efficiency, and real-world applications of probabilistic classification models. The results show that Naive Bayes is computationally efficient and effective for large-scale emotion classification, while HMM and Bayesian networks excel in sequential emotion prediction and user behavior modeling. The study highlights the advantages of probabilistic methods in sentiment analysis, while acknowledging their limitations, such as their reliance on probabilistic assumptions and the challenges of capturing deep contextual semantics. Future research should explore hybrid approaches that combine probabilistic models with deep learning techniques to improve the predictive performance and scalability of real-time sentiment analysis.

Multimodal sentiment analysis is a technical approach that integrates various modalities to analyze sentiment tendencies or emotional states. Existing challenges encountered by this approach include redundancy in independent modal features and a lack of correlation analysis between different modalities, causing insufficient fusion and degradation of result accuracy. To address these issues, this study proposes an innovative multi-channel multimodal joint learning method for image-text sentiment analysis. First, a multi-channel feature extraction module is introduced to comprehensively capture image or text features. Second, effective interaction of multimodal features is achieved by designing modality-wise interaction modules that eliminate redundant features through cross-modal cross-attention. Last, to consider the complementary role of contextual information in sentiment analysis, an adaptive multi-task fusion method is used to merge single-modal context features with multimodal features for enhancing the reliability of sentiment predictions. Experimental results demonstrate that the proposed method achieves an accuracy of 76.98% and 75.32% on the MVSA-Single and MVSA-Multiple datasets, with F1 scores of 76.23% and 75.29%, respectively, outperforming other state-of-the-art methods. This research provides new insights and methods for advancing multimodal feature fusion, enhancing the accuracy and practicality of sentiment analysis.

Most studies about sentiment analysis on microblogging usually focus on the features mining from the text. This paper presents a new sentiment analysis method by combing features from text with features from image. Bigram model is applied in text feature extraction while color and texture information are extracted from images. Considering the sentiment classification, we propose a new neighborhood classier based on the similarity of two instances described by the fusion of text and features. Experimental results show that our proposed method can improve the performance significantly on Sina Weibo data (we collect and label the data). We find that our method can not only increasingly improve the F values of the classification comparing with only used text or images features, but also outperforms the NaiveBayes and SVM classifiers using all features with text and images.

Background: Tourists visiting Lombok Island can access various sources of tourist information and can share their views and tourist experiences through social media such as positive and negative experiences.Objective: This research aims to analyze the sentiment of Lombok tourism reviews using the Smote-Tomek Link and Random Forest algorithms.Methods: The research was carried out in several stages, namely collecting the Lombok tourism dataset, text preprocessing, text weighting using the Term Frequency-Inverse Document Frequency (TF-IDF) method, data sampling using SMOTE-Tomek Link, text classification using Random Forest, and the final stage was performance testing based on accuracy.Result: The research results obtained using the Smote-Tomek Link and Random Forest methods in sentiment analysis analysis of tourist reviews about Lombok were 94%.Conclusion: The use of the Smote-Tomek Link and Random Forest methods in Lombok tourism sentiment analysis produces very good accuracy.

In recent years, vocabulary emotion processing has become immensely popular and the requirements for language emotion analysis mining and processing have become significantly abundant. The sentiment extraction and analysis work has always been very challenging; especially, the Chinese word segmentation operation is difficult to deal with effectively, the multiple combinations of implicit and explicit words make the task of sentiment analysis mining more difficult, and, in particular, the efficiency of machine analysis of language sentiment is feeble. We use some expressions and sentiment vocabulary dictionaries combined with hybrid structures and use information synergy methods to get in touch with sentiment analysis methods. We use the relevant sentiment to evaluate the explicit or implicit emotional association of the emotional connection of the vocabulary and add the unique emotional word matrix to analyze the related clustering results of the emotional words to continuously optimize and upgrade the performance, so that our sentiment analysis results are systematic in terms of efficiency and significantly improved.

Gelisen Internet teknolojileri, sosyal medya uygulamalarinin yayginlasmasi, Web 2.0’da meydana gelen gelismeler Internet kullanicilarinin kullanim aliskanliklarini degistirmistir. Gelismeler ile birlikte gunumuzde Internet kullanicilari duygu ve dusuncelerini sosyal medya uygulamalari uzerinde herhangi bir zamanda herhangi bir yerde paylasabilmektedirler. Sosyal medya kullanimi arttikca sosyal medya uzerinde olusan degerli geri bildirim verisi de giderek artmaktadir. Bu amacla sosyal medya verisinin toplanmasi, degerlendirilmesi ve yorumlanmasi giderek onem kazanmaktadir. Bu ihtiyacin karsilanmasinda, metin tabanli veriler uzerinde yorumlama ve duygu cikarsama islemleri icin “dogal dil isleme” ve “sezgi analizi” gibi yontemler kullanilmaktadir. Bu calismada mevcut sezgi analizi yontemleriyle elde edilen sonuclarin dogrulugunu ve basarisini arttirmak amaciyla ontoloji tabanli yeni bir sezgi analizi yontemi gelistirilmistir. Gelistirilen yontem ile analiz islemi oncesinde alana ozgu bilgilerin ontolojiler ile modellenmesi gerekmektedir. Bu yaklasim sayesinde klasik sezgi analizi yontemlerine gore daha dogru ve daha nitelikli sonuclarin uretilmesi saglanmistir. Gelistirilen altyapinin bir diger onemli ve yenilikci ozelligi ise sezgi analizi yonteminin Turkce dilini desteklemesidir.Gelistirilen yontem ile analiz islemi oncesinde alana ozgu bilgilerin ontolojiler ile modellenmesi gerekmektedir. Bu yaklasim sayesinde klasik sezgi analizi yontemlerine gore daha dogru ve daha nitelikli sonuclarin uretilmesi saglanmistir. Gelistirilen altyapinin bir diger onemli ve yenilikci ozelligi ise sezgi analizi yonteminin Turkce dilini desteklemesidir.

This study highlights the advantages of sentiment analysis using algorithms in understanding public opinion, especially in the context of the increasing complexity of digital content. To investigate and present the latest developments in sentiment analysis, this study uses the Systematic Literature Review (SLR) method to identify and evaluate previously developed sentiment analysis methods. The research steps involve identifying significant sentiment analysis methods, assessing advantages and disadvantages, and critically reviewing recent advances. By applying algorithms in this process, it is expected to be able to analyze and describe the development of sentiment analysis research comprehensively. Through the application of the SLR method, this study is expected to provide in-depth insights into trends, challenges, and opportunities for future research in sentiment analysis, create a better understanding of effective sentiment analysis methods, and detail the expected results that can be expected in the development of sentiment analysis.

Sentiment analysis can help extract meaningful information from these data piles from various websites and social media and measure consumers' reactions by classifying consumers' emotions as positive, negative or neutral. The success of sentiment analysis varies according to feature selection, vector space selection and machine learning method. For this reason, determining the most successful method in sentiment analysis is still controversial and important. A limited number of studies have been conducted comparing the success of various machine learning methods in sentiment analysis of hotel reviews in English. Considering this gap, the purpose of this research is to determine the most successful machine learning algorithm for sentiment analysis of hotel reviews. For this purpose, 708 reviews for 5-star hotels in Istanbul were collected manually. Obtained data were classified as positive and negative using logistic regression, k-nearest neighbor, naive Bayes and support vector machine methods. Analysis results show that the logistic regression method was the most successful classification algorithm, with an accuracy rate of 0.92. It is followed by support vector machine (0.90), naive Bayes method (0.77) and k-nearest neighbor algorithms (0.66).

Reputation systems in E-commerce (EC) play a substantial role that allows various parties to achieve mutual benefits by establishing relationships. The reputation systems aim at helping consumers in deciding whether to negotiate with a given party. Many factors negatively influence the sight of the customers and the vendors in terms of the reputation system. For instance, lack of honesty or effort in providing the feedback reviews, by which users might create phantom feedback from fake reviews to support their reputation. Moreover, the opinions obtained from users can be classified into positive or negative which can be used by a consumer to select a product. In this paper, we study online movie reviews using Sentiment Analysis (SA) methods in order to detect fake reviews. Text classification and SA methods are applied on a real conducted dataset of movie reviews. Specifically, we compare four supervised machine learning algorithms: Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN-IBK), and Decision Tree (DT-J48) for sentiment classification of reviews in two different situations without stopwords and with stopwords methods are employed. The measured results show that for both methods the SVM algorithm outperforms other algorithms, and it reaches the highest accuracy not only in text classification but also to detect fake reviews.

It is common practise to employ a contextual mining approach called sentiment analysis (SA) to glean subjective but potentially helpful information from textual data. In order to recognise, analyse, and extract answers, states, or emotions from the data, it employs Natural Language Processing (NLP), language understanding, forensics, and cognitive science. Significant progress towards a better SA model may be made using the features analysis method. In recent years, feature extractions have made extensive use of GloVe and Word2vec embedding models. They require a huge corpora of text data for training and creating accurate vectors, but they ignore emotional and contextual information in the text. Out-of-Vocabulary (OOV) words are not considered while creating these vectors, hence some information may be lost if these methods are used. The limited availability of annotated data is another obstacle to sentiment categorization. Misclassification may occur when there is a discrepancy between the review and the label. In this research, we offer a hybrid SA model capable of overcoming the challenges posed by missing ratings and reviews due to noise, out-of-context phrases, and context. To investigate and conduct sentiment and appropriate analysis, this study proposes an Autoencoder Bi-directional Recurrent Neural Network (ABRNN) based on Bi-directional Encoding from Transformers (BET). The reviews are initially categorised by their polarity ratings using the zero-shot categorization. Then, the data is fed into a pre-trained BET system to extract embeddings based on the semantics and context of sentences. The neural network, made up of expanded and hybrid LSTM, was then fed the acquired contextual embedded vectors. For extracting both global and local spatial contextual characteristics from the embedded data, this approach employs expanded approach in place of traditional approach. The complete phrase sequencing is performed with the help of Hybrid Long Short-Term Memory (HLSTM). Measures of accuracy, precision, recall, f1-score, and area under the curve (AUC) are used to assess the ABRNN model on four separate text datasets from different domains. Because of this, ABRNN may be utilised well for SA processes on social media evaluations, with no loss of data.

The Web has become an excellent source for gathering consumer opinions (more specifically, consumer reviews) about products. Consumer reviews are essential for retailers and product manufacturers to understand the general responses of customers to their products and improve their marketing campaigns or products accordingly. In addition, consumer reviews enable retailers to recognize the specific preferences of each customer, which facilitates effective marketing decisions. As the number of consumer reviews expands, it is essential and desirable to develop an efficient and effective sentiment analysis technique that is capable of extracting product features stated in consumer reviews (i.e., product feature extraction) and determining the sentiments (positive or negative semantic orientations) of consumers for these product features (i.e., opinion orientation identification). Product feature extraction is critical to sentiment analysis, because its effectiveness significantly affects the performance of opinion orientation identification, as well as the ultimate effectiveness of sentiment analysis. Therefore, this study concentrates on product feature extraction from consumer reviews. Specifically, we propose a semantic-based product feature extraction (SPE) technique that exploits a list of positive and negative adjectives defined in the General Inquirer to recognize opinion words semantically and subsequently extract product features expressed in consumer reviews. Using a prevalent product feature extraction technique and the SPE-GI technique (a variant of SPE) as performance benchmarks, our empirical evaluation shows that the proposed SPE technique outperforms both benchmark techniques.

Sentiment analysis became a hot topic, specially with the amount of opinions available in social media data. With the increasing interest in this theme, several methods have been proposed in the literature. Recent efforts have showed that there is no single method that always achieves the best prediction performance for different datasets. Additionally, novel methods have not being extensively compared with other methods and across different datasets, specially methods that are not designed to the English language. Consequently, researchers tend to accept any popular method as a valid methodology to measure sentiments, a practice that is usual in science. In this context, we propose iFeel 2.0, an online web system that implements 19 sentence-level sentiment analysis methods and allows users to easily label a dataset with all of them. iFeel aims at easing the comparison of new methods with baseline approaches and can also be helpful for those interested in using sentiment analysis, allowing them to choose an appropriate sentiment analysis method that works fine for a new dataset. We also incorporate a multiple language feature to allow methods designed for specific languages to be easily compared with a baseline approach that simply translates the input data to English and run these 19 methods. We hope this system can represent an important contribution to this field. Sentiment analysis became a hot topic, specially with the amount of opinions available in social media data.With the increasing interest in this theme, several methods have been proposed in the literature. Recent effortshave showed that there is no single method that always achieves the best prediction performance for different datasets. Additionally, novel methods have not being extensively compared with other methods and across different datasets, specially methods that are not designed to the English language.Consequently, researchers tend to accept any popular method as a valid methodology to measure sentiments, a practice that is usual in science.In this context, we propose iFeel 2.0, an online web system that implements 19 sentence-level sentiment analysis methods and allows users to easily label a dataset with all of them. iFeel aims at easing the comparison of new methods with baseline approaches and can also be helpful for those interested in using sentiment analysis, allowing them to choose an appropriate sentiment analysis method that works fine for a new dataset.We also incorporate a multiple language feature to allow methods designed for specific languages to be easily compared with a baseline approach that simply translates the input data to English and run these 19 methods. We hope this system can represent an important contribution to this field.

Sentiment analysis has become a key tool to extract knowledge from data containing opinions and sentiments, particularly, data from online social systems. With the increasing use of smartphones to access social media platforms, a new wave of applications that explore sentiment analysis in the mobile environment is beginning to emerge. However, there are various existing sentiment analysis methods and it is unclear which of them are deployable in the mobile environment. In this paper, we provide the first of a kind study in which we compare the performance of 14 sentence-level sentiment analysis methods in the mobile environment. To do that, we adapted these methods to run on Android OS and then, we measure their performance in terms of memory, CPU, and battery consumption. Our findings unveil methods that require almost no adaptations and run relatively fast as well as methods that could not be deployed due to excessive use of memory. We hope our effort provides a guide to developers and researchers interested in exploring sentiment analysis as part of a mobile application and can help new applications to be executed without the dependency of a server-side API. We also share the Android API that implements all the 14 sentiment analysis methods used in this paper.

The objective of this article was to propose a geospatial forecasting approach to analyze the textual content of social media using geospatial components and Natural Language Processing (NLP) tools. This approach has been applied to flood forecasting to mitigate future risks and flood damage since the dynamics of real-world events such as floods prompt users to discuss the topic. The approach is based on the appropriate filtering and preprocessing of information from the Twitter exchange platform. A textual preprocessing method and a new sentiment analysis method (also called opinion extraction method) have been developed to gradually build the database provided by humans. Another method was associated with sentiment analysis, the polarity method which was developed to identify the good or bad feeling attributed to a word in a sentence (positive or negative polarity). The work of this paper offers a different approach of geospatial flood prediction. The method relies on social behavior displayed and made available by users of the platform Twitter. A sentiment analysis method is developed in order to identify users’ reactions to inundations according to their geospatial location, while considering how users randomly interact on social media. This paper is innovative compared to the existing approaches, for it uses a social network to extract geospatial components, pairs them with a new data preprocessing and a sentiment analysis method, in order to predict floods on a map, by selecting relevant data. The system thus proposed in this exploration on social media allows the generation of flood forecasting maps to aid the alerting decision-making process. The flood occurrence probabilities provided by this system allow the simulation of the flood forecast distribution map for each month of the year.KeywordsGeospatial forecastingFlood forecastingSocial media explorationSentiment analysis