Rule-Based Weibo Messages Sentiment Polarity Classification towards Given Topics

In recent years, mining opinions from customer reviews has been widely explored. Aspect-level sentiment analysis is a fine-grained subtask, which aims to detect the sentiment polarity towards a particular target in a sentence. While most previous works focus on sentiment polarity classification, opinion words towards the target are also very important for that they provide details about target and contribute to judging polarity. To this end, we propose a hierarchical network for jointly modeling aspect-level sentiment classification and word-level opinion words extraction. Our joint model acquires superior performance in opinion words extraction and achieves comparable results in sentiment polarity classification on two datasets from SemEval 2014.

We investigate the effect of using sentiment expression boundaries in predicting sentiment polarity in aspect-level sentiment analysis.We manually annotate a freely available English sentiment polarity dataset with these boundaries and carry out a series of experiments which demonstrate that high quality sentiment expressions can boost the performance of polarity classification.Our experiments with neural architectures also show that CNN networks outperform LSTMs on this task and dataset.

Aspect-Based Sentiment Analysis (ABSA) plays a vital role in interpreting user opinions in customer reviews by identifying sentiment polarity linked to specific service aspects. However, traditional ABSA methods largely focus on explicit aspect mentions, often neglecting implicit aspects that are contextually implied. This limitation is especially critical in the hospitality domain, where customer reviews frequently include indirect references to hotel features. This study proposes an implicit aspect analysis framework for sentiment classification using hotel reviews, aiming to improve the detection and interpretation of implicit aspect expressions. A dataset of 2,000 TripAdvisor hotel reviews was collected and manually annotated by linguistic experts for four aspects: staff service, cleanliness, value for money, and location convenience. Each sentence was labeled as either explicitly or implicitly expressing an aspect, with associated sentiment polarity (positive or negative). A two-stage framework was developed: the first stage employed Word2Vec Skip-gram modeling and TF-IDF to construct an implicit aspect corpus; the second stage implemented machine learning and deep learning classifiers—including SVM, LR, MNB, RF, CNN, and LSTM—to predict sentiment. The hybrid feature representation combining TF-IDF and Word2Vec significantly enhanced the model's ability to detect implicit sentiments. Deep learning models, particularly LSTM and CNN, outperformed traditional classifiers in both sentiment polarity classification and implicit aspect association. Clustering results confirmed strong alignment with expert-labeled data, validating the framework’s effectiveness. The proposed framework demonstrates substantial improvements in classifying implicit aspects, offering valuable insights for service quality evaluation in hospitality and other domains relying on user-generated content.

With the recent emergence of Web-based applications and use of social networking sites, number of people are eager in expressing their views and opinions online. The sentimental analysis also referred to as opinion mining aims at processing user reviews (about products, movies, services, books, places, etc.). These reviews are often unstructured and need processing to evolve into the productive knowledge. Majority of the sentiment analysis works on the classification of opinion polarity with the use of simple classifiers. Handling diverse data distribution is one of the major issues that simple classifiers suffer. To cope up with the issue in this paper, we utilized the ensemble learners on the polarity prediction of the movie reviews. The proposed work processes the review data through some elementary steps that are conducted for the feature extraction in sentiment analysis. In addition to the feature extraction, we further perform the feature selection for the sake of dimensionality reduction. However, in contrast to the conventional simple learner, we applied the ensemble learner in the proposed model and evaluated its performance. To compare the ensemble model competence, we conducted the experiment on both individual as well as ensemble learner (random forest, AdaBoost, extra trees) and computed classification measures on both the model. IMDB dataset is used, and the polarity of a review, i.e., whether it is positive or negative, is predicted. With an extensive experimentation, it is found that results of ensemble classifiers are outperforming than individual learner in the classification of sentiment polarity.

With the widespread use of social networking media such as Weibo, it has become a relatively popular problem in academia to analyze the sentiment tendency of these tweets through artificial intelligence. The problem of text sentiment analysis based on large-scale Weibo messages has thus become particularly important. However, there are no mature sentiment analyzers in the existing research results, and there is no research for the specific message group of Weibo (which cannot exceed 140 characters per message). This paper provides a text sentiment analysis method for Weibo messages based on social networks. A sentiment lexicon for the professional field of teaching is constructed. A large amount of Twitter message data was obtained , and these messages were classified into positive, negative and fluctuating categories after filtering irrelevant noise and a text sentiment classifier built based on Naive Bayes theory. Through testing, it is found that the method proposed in this paper can better and faster classify the emotional tendency of Weibo messages, and it is of great practical significance to use this result to guide the subsequent arrangement and decision of teaching activities, which helps to promote the construction of intelligent teaching service ecosystem.

With proliferation of user-generated reviews, new opportunities and challenges arise. The advance of Web technologies allows people to access a large amount of reviews of products and services online. Knowing what others like and dislike becomes increasingly important for their decision making in online shopping. The retailers also care more than ever about online reviews, because a vast pool of reviews enables them to monitor reputations and collect feedbacks efficiently. However, people often find difficult times in identifying and summarizing fine-grained sentiments buried in the opinion-rich resources. The traditional sentiment analysis, which focuses on the overall sentiments, fails to uncover the sentiments with regard to the aspects of the reviewed entities. This dissertation studied the research problem of Aspect Based Sentiment Analysis (ABSA), which is to reveal the aspect-dependent sentiment information of review text. ABSA consists of several subtasks: 1) aspect extraction, 2) aspect term extraction, 3) aspect category classification, and 4) sentiment polarity classification at aspect level. We focused on the approach of topic models and neural networks for ABSA. First, to extract the aspects from a collection of reviews and to detect the sentiment polarity regarding the aspects in each review, we proposed a few probabilistic graphical models, which can model words distribution in reviews and aspect ratings at the same time. Second, we presented a multi-task learning model based on long-short term memory and convolutional neural network for aspect category classification and aspect term extraction. Third, for aspect-level sentiment polarity classification, we developed a gated convolution neural network, which can be applied to aspect category sentiment analysis as well as aspect target sentiment analysis.

The development of digital technology has brought significant changes across various sectors of life, including transportation. One of the most popular modes of transportation among the public today is online motorcycle taxis, such as Gojek. Gojek continues to innovate to meet customer needs more effectively and to expand its range of services. This study aims to identify the number of positive, neutral, and negative sentiments in a user review dataset, as well as to evaluate the performance of the algorithms used—namely, SVM and Random Forest. The analysis was conducted on 10,000 customer reviews from the Play Store application, resulting in 2,057 positive sentiments, 1,135 neutral sentiments, and 6,295 negative sentiments. The classification model compared the SVM algorithm with the Random Forest algorithm, and the results show that Random Forest achieved better performance, with 91% accuracy compared to SVM’s 89%. These findings demonstrate that Random Forest performs better in handling word distribution within review texts than the SVM method.

The text mining literature shows a growing body of work concerned with the automatic identification of sentiment in text. Sentiment polarity classification is one of the most important text mining tasks. The typical approach to polarity classification uses lexicons to count word usage from linguistic or emotional aspects. One of the most widely used lexicons is the Linguistic Inquiry and Word Count (LIWC). LIWC assigns words to categories (e.g., positive emotion) based on a lexicon of words associated with psycholinguist categories. It has been widely used in polarity classification task with good results. However, it only accounts for word count, discarding the text structure and ignoring important semantic relationships between words. In this work, we present LIWBC, an algorithm to count bigrams using the lexicon provided by LIWC. The goal is to incorporate text structure information to improve the polarity classification task with LIWC lexicon. We conducted experiments to evaluate LIWBC with two real datasets: the first one consists of blogger posts; the second one is the movie reviews dataset, which contains full-text movie reviews from IMDB. Both datasets were processed with LIWC and LIWBC. After that, we ran four classification algorithms in the data processed by LIWC and LIWBC. The SVM algorithm executed with LIWBC data yielded the best result in both datasets. The F1 score of SVM in blogger posts and movie reviews dataset had an improvement of 2.2% and 2.5%, respectively.

In this article we introduce an empirical study of multilingual and multi-topic opinion classification. The particularity relies on the reviews that are written in different languages and refer to different but semantically close topics: Restaurants and Hotels. Our key objective is to emphasize the ability of a deep learning model to establish the sentiment polarity of reviews and topics Classification in a multilingual environment without any prior knowledge. For this work, we use unstructured text data, collected from the web, written in French, English and Greek (a less opinion-present language). The incorporate corpus-based input is raw, used without any pre-processing, translation, annotation nor additional knowledge features. For the machine learning approach, we use two different deep neural networks, Convolutional Neural Networks (CONVNETS) and Recurrent Neural Networks (RNNs). The learning model exploits n-gram level information, and achieves high accuracy for sentiment polarity and topics classification according to the experimental tests and results. From our hypothesis, we argue that the multilingual environment composed of reviews in semantically close domains, does not impact the network performance, and lead us to deduce that semantic features extraction with ConvNets and RNNs are language and context independent. Following these results, we tend to promote the inception of simple yet powerful approach for feeding deep networks in multilingual context.

The rapid growth of financial technology (fintech) services in Indonesia has significantly transformed digital transaction behavior, with digital wallets such as DANA becoming widely adopted. Despite high usage rates, public discourse on social media frequently highlights both positive experiences and recurring concerns related to system reliability and data security. Twitter, as a real-time public communication platform, provides a rich source of user-generated content that reflects public perception toward fintech services. This study investigates public sentiment toward DANA fintech services in Indonesia through sentiment analysis of Twitter data using machine learning approaches. Purpose:This study aims to analyze public perception of DANA fintech services based on sentiment expressed on Twitter and to compare the performance of several machine learning classification algorithms in identifying positive, neutral, and negative sentiments in Indonesian-language social media texts. Methods/Study design/approach: The study employs a quantitative descriptive approach using text mining and sentiment analysis techniques. Twitter data were collected via the Twitter API over a specified period and processed through preprocessing stages including text cleaning, tokenization, stopword removal, and stemming. Feature extraction was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Sentiment classification was conducted using Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest algorithms. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Result/Findings:The results indicate that public sentiment toward DANA is predominantly positive, driven by perceptions of transaction convenience and speed, followed by neutral sentiment, while negative sentiment is mainly associated with system disruptions and data security issues. Among the evaluated algorithms, SVM achieved the highest classification accuracy (91.4%), outperforming Random Forest and KNN. These findings demonstrate that SVM is more effective for sentiment classification of short Indonesian-language texts on social media platforms. Novelty/Originality/Value:This study contributes to the growing body of sentiment analysis research in Indonesia by providing empirical evidence on public perception of fintech services using machine learning techniques. By systematically comparing multiple classification algorithms in the context of Indonesian Twitter data, the study offers methodological insights and practical implications for fintech service providers in improving service quality, communication strategies, and user trust.

Data-driven integration of multiple sentiment dictionaries for lexicon-based sentiment classification of product reviews

Sentiment analysis algorithms are often used for sentiment polarity classification and topic classification. Sentiment polarity classification is used a lot for review analysis. Gross prediction of the movie combined with people’s anticipations, if available before release of the movie, can hugely benefit the producer as well as the distributor in increasing their profits. In this paper, we particularly focus on gross prediction for the movie and the reliability of that prediction by analyzing user reviews on various prerelease events. For that, we propose a modified approach built on top of word2vec achieving comparable results to doc2vec, with significantly less running time and space complexity than doc2vec.

Deep convolutional neural networks excel at sentiment polarity classification, but tend to require substantial amounts of training data, which moreover differs quite significantly between domains. In this work, we present an approach to feed generic cues into the training process of such networks, leading to better generalization abilities given limited training data. We propose to induce sentiment embeddings via supervision on extrinsic data, which are then fed into the model via a dedicated memory-based component. We observe significant gains in effectiveness on a range of different datasets in seven different languages.

LLarge language models (LLMs) demonstrate strong natural language processing capabilities but come with significant computational costs, raising questions about their practical utility compared to small language models (SLMs). This study systematically compares SLMs (DistilBERT, ELECTRA) and LLMs (Flan-T5, Flan-UL2) on two customer review analysis tasks: sentiment polarity classification and product correlation analysis. Our results show that while LLMs outperform in sentiment classification, they do so at a much higher computational cost, whereas fine-tuned SLMs excel in domain-specific correlation analysis with greater efficiency. To balance accuracy and cost, we propose a context-enhanced hybrid (CE-Hybrid) model, which refines traditional hybrid methods by enriching LLM input with SLM-generated insights, reducing redundant computation while maintaining accuracy. Our findings quantify the trade-offs between model performance and resource efficiency, offering actionable insights for businesses to optimize AI deployment. These results have significant implications for real-world applications such as e-commerce, customer service automation, and business analytics.

Sentiment analysis or opinion mining used to capture the community's attitude who have experienced the specific service/product. Sentiment analysis usually concentrates to classify the opinion of whole document or sentence. However, in most comments, users often express their opinions on different aspects of the mentioned entity rather than express general sentiments on entire document. In this case, using aspect-based sentiment analysis (ABSA) is a solution. ABSA emphases on extracting and synthesizing sentiments on particular aspects of entities in opinion text. The previous studies have difficulty working with aspect extraction and sentiment polarity classification in multiple domains of review. We offer an innovative deep learning approach with the integrated construction of bidirectional Long Short Term Memory (BiLSTM) and Convolutional Neural Network (CNN) for multidomain ABSA in this article. Our system finished the following tasks: domain classification, aspect extraction and opinion determination of aspect in the document. Besides applying GloVe word embedding for input sentences from mixed Laptop_Restaurant domain of the SemEval 2016 dataset, we also use the additional layer of POS to pick out the word morphological attributes before feeding to the CNN_BiLSTM architecture to enhance the flexibility and precision of our suggested model. Through experiment, we found that our proposed model has performed the above mentioned tasks of domain classification, aspect and sentiment extraction concurrently on a mixed domain dataset and achieved the positive results compared to previous models that were performed only on separated domain dataset.