Aspect-based Sentiment Analysis using Dependency Parsing

CoreNLP dependency parsing and pattern identification for enhanced opinion mining in aspect-based sentiment analysis

Distance-reconstructed dependency enhanced aspect-based sentiment analysis with sentiment strength

Aspect-Based Sentiment Analysis (ABSA) is a Natural Language Processing (NLP) task that extracts referred aspects from text and assigns polarities to opinions about those aspects. Most research on ABSA focuses on English. Only a few ABSA works deal with the Portuguese language. In this work, we used BERTimbau to create a Question-Answer approach to ABSA in Portuguese. First, we post-trained this model with text from the same domain as our target corpus. Then, we constructed an auxiliary sentence from the aspect and converted ABSA to a sentence-pair classification task, such as question answering (QA) and natural language inference (NLI). Our experiments show that ABSA based on BERT for Portuguese achieved Balanced Accuracy (BACC) of 77% on a corpus of reviews about the accommodation sector using a post-trained model with QA approach.

The most important task in aspect-based sentiment analysis (ABSA) is the aspect and sentiment word extraction. It is a challenge to identify and extract each aspect and it specific associated sentiment word correctly in the review sentence that consists of multiple aspects with various polarities expressed for multiple sentiments. By exploiting the dependency relation between words in a review, the multiple aspects and its corresponding sentiment can be identified. However, not all types of dependency relation patterns are able to extract candidate aspect and sentiment word pairs. In this paper, a preliminary study was performed on the performance of different type of dependency relation with different POS tag patterns in pre-extracting candidate aspect from customer review. The result contributes to the identification of the specific type dependency relation with it POS tag pattern that lead to high aspect extraction performance. The combination of these dependency relations offers a solution for single aspect single sentiment and multi aspect multi sentiment cases.

The proliferation of user contributed content in e-commerce, social media, and review sites have increased the demand for more accurate approaches to polarity detection. This need is met by Aspect-Based Sentiment Analysis (ABSA) that provides sentiments related to certain aspects of an entity which is useful for e-commerce, healthcare, and social media analysis. In this work, we propose the use of Hierarchical Attention Networks (HAN) for ABSA due to their ability to model at multiple granularities to enhance aspect-level sentiment classification. The presented framework improves position-sensitive embeddings and uses multi-head attentions to boost the scalability, interpretability, and performance of the cased model on multilingual and domain-shifted datasets. On various datasets, the proposed method showed better accuracy (93%), precision (92%), and F1-score (92%) than conventional methods. The model also performs well in dealing with implicit features and subtle sentiment patterns, which is further accompanied by attention visualization for better understanding. This research establishes new standards in ABSA, solving scalability and domain adaptability issues and opening the way to its application in large-scale sentiment analysis.

Aspect Based Sentiment Analysis (ABSA) provides further insight into the analysis of social media. Understanding user opinion about different aspects of products, services or policies can be used for improving and innovating in an effective way. Thus, it is becoming an increasingly important task in the Natural Language Processing (NLP) realm. The standard pipeline of aspect-based sentiment analysis consists of three phases: aspect category detection, Opinion Target Extraction (OTE) and sentiment polarity classification. In this article, we propose an alternative pipeline: OTE, aspect classification, aspect context detection and sentiment classification. As it can be observed, the opinionated words are first detected and then are classified into aspects. In addition, the opinionated fragment of every aspect is delimited before performing the sentiment analysis. This paper is focused on the aspect classification and aspect context detection phases and proposes a twofold contribution. First, we propose a hybrid model consisting of a word embeddings model used in conjunction with semantic similarity measures in order to develop an aspect classifier module. Second, we extend the context detection algorithm by Mukherjee et al. to improve its performance. The system has been evaluated using the SemEval2016 datasets. The evaluation shows through several experiments that the use of hybrid techniques that aggregate different sources of information improves the classification performance.

The tourism sector in Indonesia experienced growth and made a positive contribution to the national economy, but this growth has yet to reach its target. Therefore, the government of Indonesia has implemented a sustainable tourism development program by establishing ten priority tourism destinations. Aspect-based sentiment analysis (ABSA) towards tourist attraction reviews can assist the government in developing potential goals. The ABSA process compares with two deep learning models (LSTM and Bi-LSTM), which are considered to obtain good performance in text analysis. The shortcomings of previous ABSA research should have examined the performance of the aspect classification and sentiment classification models sequentially. This makes the performance obtained from the ABSA task invalid. Thus, this study is conducted to determine the version of the aspect classification model and the sentiment classification model individually and simultaneously. This study aims to develop an aspect-based tourist attraction sentiment analysis as an intelligent system solution for sustainable tourism development by applying the binary relevance mechanism and the best deep learning model from LSTM or Bi-LSTM. The test results showed that Bi-LSTM was superior in aspect and sentiment classification individually and simultaneously. Likewise, the aspect classification and sentiment classification test results sequentially Bi-LSTM outperformed that of LSTM. The average accuracy and f1 score of Bi-LSTM are 92.22% and 71,06%. Meanwhile, LSTM obtained 90,63% of average precision and 70,4% of f1 score.

The analysis of user generated content on social media and the accurate specification of user opinions towards products and events is quite valuable to many applications. With the proliferation of Web 2.0 and the rapid growth of user-generated content on the web, approaches on aspect level sentiment analysis that yield fine grained information are of great interest. In this work, a classifier ensemble approach for aspect based sentiment analysis is presented. The approach is generic and utilizes latent dirichlet allocation to model a topic and to specify the main aspects that users address. Then, each comment is further analyzed and word dependencies that indicate the interactions between words and aspects are extracted. An ensemble classifier formulated by naive bayes, maximum entropy and support vector machines is designed to recognize the polarity of the user's comment towards each aspect. The evaluation results show sound improvement compared to individual classifiers and indicate that the ensemble system is scalable and accurate in analyzing user generated content and in specifying users' opinions and attitudes.

This research has used the techniques of Natural Language Processing to explore how to do sentiment analysis on social media by using three algorithms, G-LSTM, RMDEASD, and BERT. It tested how the models perform on a different set of domains like disaster management, corporate performance, and consumer behavior for the purpose of sentiment classification. The study utilized more than 500,000 social media posts as the dataset, with accuracy rates of 88.4% for G-LSTM, 85.7% for RMDEASD, and 91.2% for BERT. Results reveal that BERT surpassed the other models in terms of accuracy and contextual understanding in aspect-based sentiment analysis. Moreover, the hybrid model G-LSTM was effective in disaster-related tweets by achieving a 92.3% accuracy in real-time sentiment classification. Comparisons with related work show that the proposed models significantly improve the accuracy and robustness of sentiment analysis over traditional machine learning methods. Challenges such as sentiment classification in low-resource languages are also addressed, providing insights on how to improve model applicability in diverse linguistic contexts. The findings add to the increasing body of research in sentiment analysis and indicate its potential applications in several industries, such as healthcare, marketing, and public opinion analysis.

Sentiment analysis is becoming rapidly important for exploring social media Bangla text. The lack of sufficient resources is considered to be an important challenge for Aspect Based Sentiment Analysis (ABSA) of the Bangla language. The ABSA is a technique that divides the text and defines its sentiment based on its aspects. In this paper, we developed a high-quality Bangla ABSA annotated dataset namely BANGLA_ABSA. The datasets are labelled with aspects category and their respective sentiment polarity to do the ABSA task in Bangla. Four open domains namely Restaurant, Movie, Mobile phone, and Car are considered to make the dataset. The datasets are called Restaurant_ABSA, Movie_ABSA, Mobile_phone_ABSA, and Car_ABSA respectively that contain 801, 800, 975, and 1149 comments. All the comments are either complex or compound sentences. We created the dataset manually and annotated the same by exerting opinions. We organized the dataset as three tuples in Excel format namely 〈Id, Comment, {Aspect category, Sentiment Polarity}〉. These data are very important that facilitate the efficient handling of sentiment for any machine learning and deep learning research, especially for Bangla text.

Aspect-based sentiment analysis (ABSA) aims to identify the sentiment of an aspect in a given sentence and thus can provide people with comprehensive information. However, many conventional methods need help to discover the linguistic knowledge implicit in sentences. Additionally, they are susceptible to unrelated words. To improve the performance of the model in the ABSA task, a multi-task sentiment analysis model based on Bidirectional Encoder Representation from Transformers (BERT) and a Knowledge Graph (SABKG) is proposed in this paper. Expressly, part-of-speech information is incorporated into the output representation of BERT, thereby obtaining textual semantic information through linguistic knowledge. It also enhances the textual representation to identify the aspect terms. Moreover, this paper constructs a knowledge graph of aspect and sentiment words. It uses a graph neural network to learn the embeddings in the triplet of “aspect word, sentiment polarity, sentiment word”. The constructed graph improves the contextual relationship between the text’s aspect and sentiment words. The experimental results on three open datasets show that the proposed model can achieve the most advanced performance compared with previous models.

Sentiment analysis is a method of analyzing data to identify its intent. It identifies the emotional tone of a text body. Aspect-based sentiment analysis is a text analysis technique that identifies the aspect and the sentiment associated with each aspect. Different organizations use aspect-based sentiment analysis to analyze opinions about a product, service, or idea. Traditional sentiment analysis methods analyze the complete text and assign a single sentiment label to it. They do not handle the tasks of aspect association, dealing with multiple aspects and inclusion of linguistic concepts together as a system. In this article, AlgoDM, an algorithm to perform aspect-based sentiment analysis is proposed. AlgoDM uses a novel concept of IDistance matrix to extract aspects, associate aspects with sentiment words, and determine the sentiment associated with each aspect. The IDistance matrix is constructed to calculate the distance between aspects and the words expressing the sentiment related to the aspect. It works at the sentence level and identifies the opinion expressed on each aspect appearing in the sentence. It also evaluates the overall sentiment expressed in the sentence. The proposed algorithm can perform sentiment analysis of any opinionated text.

IFusionQuad: A novel framework for improved aspect-based sentiment quadruple analysis in dialogue contexts with advanced feature integration and contextual CloBlock

User-generated content (UGC) is a promising tool to assist consumers in hotel decision-making in an online environment. Existing studies on extracting sentiment opinions based on dependency parsing in aspect-based sentiment analysis (ABSA) are limited and rarely consider the importance of keywords and risk preferences of consumers to positive and negative ratings. To fill this gap, this study develops a decision-making model to select hotels based on UGC. First, the TextRank method is used to determine criteria and keywords, and 11 rules are proposed to identify potential sentiment opinions about specific keywords based on dependent parsing in ABSA. Then, a criteria value calculation method is proposed by combining sentiment scores and aspect-level ratings, where prospect theory is employed to portray different risk preferences of consumers for positive and negative ratings. Next, criteria values are input into a fuzzy inference system (FIS), and 29 linguistic rules are proposed to model the non-linear relationship between criteria and hotel performance. The applicability of the proposed method is verified by ranking 11 hotels in Chengdu city on Tripadvisor.com. Comparative analysis is given to demonstrate the effectiveness of the proposed model. Management implications are provided for practitioners to improve hotel management quality.

All businesses, including car manufacturers, need to understand what aspects of their products are perceived as positive and negative based on user reviews so that they can make improvements for the negative aspects and maintain the already positive aspects of their products. One of the available tools for this task is Sentiment Analysis. The traditional document-level and sentence-level sentiment analysis will only classify each document / sentence into a class. This approach is incapable of finding the more fine-grained sentiment for a specific aspect of interest, for example, comfort, price, engine, paint, etc. Therefore, in this case, Aspect-based Sentiment Analysis is used. A total of 22.702 rows of car review data are scraped from the Edmunds website (www.edmunds.com) for a specific car manufacturer. Dependency Parsing and noun phrase extraction were carried out using the SpaCy module in Python, and VADER sentiment analysis was used to determine the polarity of the sentiment for each noun phrase. Results showed that the vast majority of the sentiments are on the positive aspects: comfortable to drive, good fuel economy / mileage, reliability, spaciousness, value for money, helpful rear camera, quiet ride, good acceleration, well-designed, good sound system, and solid build. The results for the negative aspects have some similar aspects with those in the positive class but has a very low frequency. This finding means that the vast majority of the users are satisfied with multiple aspects of the produced cars. The limitation of this research and future research direction are discussed.