Implicit Aspect Extraction for Sentiment Analysis: A Survey of Recent Approaches

Aspect-based sentiment analysis (ABSA) is currently among the most vigorous areas in natural language processing (NLP). Individuals, private and government institutions are increasingly using media sources for decision making. In the last decade, aspect extraction has been the most essential phase of sentiment analysis (SA) to conduct an abridged sentiment classification. However, previous studies on sentiment analysis mostly focused on explicit aspects extraction with limited work on implicit aspects. To the best of our knowledge, this is the first systematic review that covers implicit, explicit, and the combination of both implicit and explicit aspect extractions. Therefore, this systematic review has been conducted to, 1) identify techniques used for extracting implicit, explicit, or both implicit and explicit aspects; 2) analyze the various evaluation metrics, data domains, and languages involved in the implicit and explicit aspect extraction in sentiment analysis from years 2008 to 2019; 3) identify the key challenges associated with the techniques based on the result of a comprehensive comparative analysis; and finally, 4) highlight the feasible opportunities for future research directions. This review can be used to assist novice and prominent researchers to understand the concept of both implicit and explicit aspect extractions in aspect-based sentiment analysis domain.

This paper focuses on aspect extraction which is a sub-task of Aspect-based Sentiment Analysis. The goal is to report an extraction method of financial aspects in microblog messages. Our approach uses a stock-investment taxonomy for the identification of explicit and implicit aspects. We compare supervised and unsupervised methods to assign predefined categories at message level. Results on 7 aspect classes show 0.71 accuracy, while the 32 class classification gives 0.82 accuracy for messages containing explicit aspects and 0.35 for implicit aspects.

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.

The existing methods for Chinese sentiment Labeling mainly relies on the artificial sentiment corpus, but a sentiment word in the corpus may not be sentiment words in different sentences. This paper proposes a new method to label the words in the sentences by combining deep convolution neural network with sequential algorithm., We first extract the aspects comprised by words vectors, part of speech vectors, dependent syntax vectors to train the deep convolution neural network, and then employ the sequential algorithm to obtain the sentiment annotation of the sentence. Experimental results verify that our method is effective for sentiment labeling. Considering that the identification of the implicit aspects can improve the completeness of sentiment analysis, we suggest to construct the tuples including aspect, sentiment shifter, sentiment intensity, sentiment words after obtaining the sentiment labels for each word in the sentence. We develop new algorithm for implicit aspect identification by taking the two key factors of the aspects as a topic and the match degree of aspects and sentiment words, and the human language habit. The experiment demonstrates that the algorithm can effectively identify the implicit aspect. In this paper, we solve the problem of sentiment labeling and implicit aspect recognition in sentiment analysis. As a new tool for sentiment analysis, our method can be applied to the enterprise management information analysis, such as product online review, product online reputation, brand image and consumer preference management, and can also be used for the sentiment analysis of large-scale text data.

In the era of e-commerce, tremendous product reviews can provide quick and valuable insight into the market trends. Aspect-based sentiment analysis (ABSA) on product reviews is becoming increasingly important for both companies and consumers. Implicit aspect extraction, a subtask of ABSA, aims to identify the aspect of a review sentence without explicit aspect terms. For Chinese online reviews, this paper proposes a fine-tuning BERT based method to extract implicit aspects. Fine-tuning BERT performs well on a variety of natural language processing tasks. Using labeled data, the hidden layer parameters of pre-training BERT are fine-tuned in a supervised manner to generate fine-tuning BERT. Through experiments on Chinese online reviews of clothing, it is believed that fine-tuning BERT can improve the effect of implicit aspects identification.

Sentiment analysis is a popular technique to identify the sentiment of a piece of text. Several different domains have been targeted by sentiment analysis research, such as Twitter, movie reviews, and mobile app reviews. Although several techniques have been proposed, the performance of current sentiment analysis techniques is still far from acceptable, mainly when applied in domains on which they were not trained. In addition, the causes of wrong classifications are not clear. In this article, we study how sentiment analysis performs on game reviews. We first report the results of a large-scale empirical study on the performance of widely used sentiment classifiers on game reviews. Then, we investigate the root causes for the wrong classifications and quantify the impact of each cause on the overall performance. We study three existing classifiers: <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stanford CoreNLP</monospace> , <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NLTK</monospace> , and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SentiStrength</monospace> . Our results show that most classifiers do not perform well on game reviews, with the best one being <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NLTK</monospace> (with an AUC of 0.70). We also identified four main causes for wrong classifications, such as reviews that point out advantages and disadvantages of the game, which might confuse the classifier. The identified causes are not trivial to be resolved and we call upon sentiment analysis and game researchers and developers to prioritize a research agenda that investigates how the performance of sentiment analysis of game reviews can be improved, for instance by developing techniques that can automatically deal with specific game-related issues of reviews (e.g., reviews with advantages and disadvantages). Finally, we show that training sentiment classifiers on reviews that are stratified by the game genre is effective.

&lt;p&gt;Sentiment Analysis (SA) or Opinion Mining is the process of analysing natural language texts to detect an emotion or a pattern of emotions towards a certain product to make a decision about that product. SA is a topic of text mining, Natural Language Processing (NLP) and web mining disciplines. Research in SA is currently at its peak given the amount of data generated from social media networks. The concept is that consumers are expressing exactly what they need, want and expect from a product but on the other hand the companies don’t have the tools to analyse and understand these feelings to satisfy these consumers accordingly. &lt;/p&gt;&lt;p&gt;One of the applications that generate a high rate of reactions and sentiments in social networks is Instagram. This study focuses on analysing the reactions generated by the top 50 fashion houses on Instagram given their top 20 images with the highest number of likes. The approach taken in this study is to qualify the visual aesthetics of fashion images and to establish why some succeed on social media more than others. &lt;/p&gt;&lt;p class="Els-Abstract-text"&gt;The basic question asked in this paper is whether there are certain visual aesthetics that appeal more to the user and are therefore more successful on social media than others as determined by a measure we introduce, ‘Social Value’. To do so, a sentiment analysis tool is developed to measure the proposed social value of each image. An input of comments from each image will be processed. Each comment will go through a pre-processing phase; each word will be placed through a lexicon to identify if it is positive or negative. The output of the lexicon is a score value assigned to each comment to identify its degree of positivity, negativity, or it has no effect on the social value. Adding to these results, the number of likes and shares would also be taken into consideration quantifying the image’s value. A cumulative result is then produced to determine the social value of an image.&lt;/p&gt;

People frequently use the social media site Twitter to share their thoughts and show their emotions on various occasions. Data analysis using sentiment analysis can retrieve the sentiment that the data contains. Twitter sentiment analysis applies sentiment analysis to data from tweets on the social media platform in order to identify user sentiments. The amount of study in this area has steadily increased over the last few decades. The complex format of tweets, which makes processing harder, is the cause of this. The very small size of the tweet format creates a whole new set of issues, such as the use of slang and acronyms. In this essay, we evaluate a few publications on sentiment analysis research on Twitter, detailing the methodology used and.

Abstract(NLP) is an acronym for natural language processing. It refers to the branch of computer science—and more specifically, the branch of artificial intelligence concerned with giving computers the ability to understand the text and spoken words in the similar way human beings can do. Emotion detection (ED) is a sentiment analysis branch dedicated to emotional extraction and analysis. Speech, facial expressions, and body language may determine human emotions efficiently. Emotion detection is an emerging field of research in sentiment analysis for effective interaction between humans and computers. Emotion detection and text recognition is a recent study subject that is strongly connected with sentiment analysis. Positive, neutral, or negative sensations are identified in a text, whereas the objective of the emotional analysis is to discover and recognize different kinds of feelings through the use of words such as wrath, disgust, fear, happiness, sorrow, and surprise. A multitude of research in the field of text mining and analysis is being conducted due to the convenience of data acquisition and the enormous benefits offered by it. This article examines the concept of emotion detection (ED) from text, image and outlines the primary approaches taken in the construction of text and image-based emotion detection (ED) systems by researchers. This article further discusses about how we are going to use sentiment analysis in emotion detection to analyze what exactly is the intent of the sender toward the receiver, how we are going to apply emotion detection (ED) to bifurcate the various emotions of the sender. This research paper proposes the discussion related to the handouts, findings, datasets used, and the combined outcome obtained after analyzing the dataset. This research is based on two (ED) emotion detection techniques: I) Text based (ED) II) Image based (ED). Researcher aims to analyze a plain text and images on any platform, be it reviews, comments or image on a blog post or social media applications, etc. It further focuses on detecting the emotion of the sender or receiver by using sentiment analysis.( Sentiment Analysis: The Go-To Guide monkeylearn.com), Sentiment Analysis.KeywordsNatural language processingSentiment analysisEmotion detectionFace recognitionImage processingComputer vision

Social media are considered an excellent source of information and can provide opinions, thoughts and insights toward various important topics. Sentiment analysis becomes a hot topic in research due to its importance in making decisions based on opinions derived from analyzing the user’s contents on social media. Although the Arabic language is one of the widely spoken languages used for content sharing across the social media, the sentiment analysis on Arabic contents is limited due to several challenges including the morphological structures of the language, the varieties of dialects and the lack of the appropriate corpora. Hence, the rapid increase in research in Arabic sentiment analysis is grown slowly in contrast to other languages such as English. The contribution of this paper is twofold: First, we introduce a corpus of forty thousand labeled Arabic tweets spanning several topics. Second, we present three deep learning models, namely CNN, LSTM and RCNN, for Arabic sentiment analysis. With the help of word embedding, we validate the performance of the three models on the proposed corpus. The experimental results indicate that LSTM with an average accuracy of 81.31% outperforms CNN and RCNN. Also, applying data augmentation on the corpus increases LSTM accuracy by 8.3%.

Since the research of sentiment analysis is mostly concentrated in the field of sentiment analysis on Weibo, and there is less research on sentiment analysis of financial text, this thesis proposes a financial sentiment analysis model based on pre-training and TextCNN. First, the pre-trained model is used to initially extract the emotional features of the text. It can extract text features well, and can extract information between words at arbitrary intervals when processing text sequences. Then use the improved TextCNN to construct a sentiment analysis network to further extract the sentiment features of the text, effectively identify the sentiment of the text, and complete the sentiment analysis of financial text. This thesis conducts experiments on a balanced corpus data set based on financial texts, and compares it with other classic sentiment analysis algorithms. Experimental results show that the proposed method works best in the field of financial text sentiment analysis.

Aspects extraction is the key task in the sentiment analysis problem, which includes extraction of both explicit and implicit aspects. Identifying implicit aspects is not a new task in sentiment analysis, but it still presents many challenges. Numerous studies have addressed the issue and offered different approaches, but there are still fundamental challenges as follows: solving the context word problem; the domain-specific problem; and the implicit aspects represented by sentiment words that do not appear explicitly in the text. This paper proposes a method for identifying implicit aspects of sentiment words. To address these above challenges, the system is built on the foundation of deep learning with context vectors, dependency grammar, anaphora coreference resolution, and sentiment ontology. The dependency parser and the entity coreference resolver are adopted to filter out the sentiment–aspect pairs data generated from embedding context words. Then, a fine-tuned technique is applied, and the classifier corresponding to each sentiment word is built to identify implicit aspects. This combination has produced positive results, reaching almost 90% accuracy.

TripAdvisor is one of the most popular e-commerce platforms in the tourism sector in Indonesia. TripAdvisor give Traveler Choice Award every year in Indonesia through user reviews. However, online text-based reviews are often associated only with evaluation scores that do not pay attention to the context and meaningful content of the review itself, either explicitly or implicitly. Moreover, the sentence structure of the review can have an impact on the goal of the target sentiment which is nothing but an aspect of the review itself. This research discusses aspect based sentiment analysis for explicit and implicit aspects. This research starting with taking the TripAdvisor website restaurant product review dataset to measure customer satisfaction based on four aspect categories of Ambience, Food, Service, and Price. Furthermore, the aspect word extraction and opinion word extraction processes in the case of explicit sentences for simple, compound, complex, and compound-complex sentence structures are carried out using grammatical rule extraction. This research also works on implicit sentence cases for simple sentence structures. Aspect categorization process uses hybrid approach. Aspect and opinion keyword extraction process uses the ELMo-Wikipedia. Then WordNet and TF-ICF are used to expand the meaning of aspect and opinion that has been taken. The last stage is the aspect based sentiment analysis process, both explicit and implicit sentences using SentiCircle. This research can produce two evaluations of sentiment classification, namely positive and negative. The results of the aspect extraction obtained an evaluation of the aspect categorization for each precision 0.82, recall 0.87, and f1measure 0.86. Meanwhile, the results of the sentiment analysis showed that the respective evaluations for precision 0.87, recall 0.92, and f1-measure 0.89.

With the rapid growth of the Web text data,mining and analyzing these text data,especially the online review data posted by the users,can greatly help better understand the users' consuming habits and public opinions,and played an important role in decision-making for the enterprises and the government.This survey first introduced the motivation,research problems and goals of sentiment analysis,and presents some basic technologies used in sentiment analysis.It then described one of the major tasks in sentiment analysis,subjective sentence detection,by reviewing and analyzing some recent work in this area.Next,it focused on another important task in sentiment analysis,opinion classification,and discussed two leading feature extraction techniques for opinion classification,sentimental word based and frequent pattern based methods.Furthermore,it also introduced several other relevant sentiment analysis problems.Finally,the current status,remaining challenges,and future directions in the field of sentiment analysis was summarized.With the rapid growth of the Web text data,mining and analyzing these text data,especially the online review data posted by the users,can greatly help better understand the users' consuming habits and public opinions,and plays an important role in decision-making for the enterprises and the government.This survey first introduces the motivation,research problems and goals of sentiment analysis,and presents some basic technologies used in sentiment analysis.It then describes one of the major tasks in sentiment analysis,subjective sentence detection,by reviewing and analyzing some recent work in this area.Next,it focuses on another important task in sentiment analysis,opinion classification,and discusses two leading feature extraction techniques for opinion classification,sentimental word based and frequent pattern based methods.Furthermore,it also introduces several other relevant sentiment analysis problems.Finally,the paper summarizes the current status,remaining challenges,and future directions in the field of sentiment analysis.

Aspect-based opinion mining is one among the thought-provoking research field which focuses on the extraction of vivacious aspects from opinionated texts and polarity value associated with these. The principal aim here is to identify user sentiments about specific features of a product or service rather than overall polarity. This fine-grained polarity identification about myriad aspects of an entity is highly beneficial for individuals or business organizations. Extricating these implicit or explicit aspects can be very challenging and this paper elaborates copious aspect extraction techniques, which is decisive for aspect-based sentiment analysis. This paper presents a novel idea of combining several approaches like Part of Speech tagging, dependency parsing, word embedding, and deep learning to enrich the aspect-based sentiment analysis specially designed for Twitter data. The results show that combining deep learning with traditional techniques can produce excellent results than lexicon-based methods.