Sentiment analysis techniques in recent works

Sentiment analysis (SA) is one of the most active and rapidly developing research areas in both information retrieval and text mining fields of computer science. SA is the process of analysing, handling, generalizing, and reasoning about user sentiments, opinions, and emotions hidden within the text by using sentiment. There are various categories of levels of SA: (1) document-level, (2) sentence-level, and (3) aspect-level. This paper is focused on sorting out some Aspect-based sentiment analysis (ABSA) works. ABSA is one of the primary tasks in sentiment analysis, which involves identifying the expression of an opinion or sensation of an object in a particular dimension or feature. ABSA involves two primary subtasks: (1) aspect extraction and (2) aspect-based sentiment classification. In contrast to document-and sentence-based sentiment analysis, ABSA simultaneously considers sentiment and target information. Generally, the ABSA methods can be categorized into Transformer based ABSA and Graph Neural Network based ABSA. This paper introduces some popular frameworks, datasets, and evaluation metrics of ABSA tasks based on the above-proposed classification and methodology.

In the ever-expanding digital landscape, the abundance of user-generated content on consumer platforms such as Booking and TripAdvisor offers a rich source of information for both travellers and hoteliers. Sentiment analysis, a fundamental research task of natural language processing (NLP) is used for mining sentiments and opinions within this vast reservoir of text reviews. A more specific type of sentiment analysis, i.e., aspect-based sentiment analysis (ABSA), is used when processing customer reviews is required. In ABSA, we aim to capture aspect-level sentiments and intricate relationships between various aspects within reviews. This article proposes a novel approach to ABSA by introducing a novel technique of word sense disambiguation (WSD) and integrating it with the Transformer architecture bidirectional encoder representations from Transformers (BERT) and graph convolutional networks (GCNs). The proposed approach resolves the intriguing ambiguities of the words and represents the review data as a complex graph structure, facilitating the modeling of intricate relationships between different aspects. The combination of bidirectional long short-term memory (BiLSTM) and GCN proves effective in capturing inter-dependencies among various aspects, providing a nuanced understanding of customer sentiments. The experiments are conducted on the RABSA dataset (an enhanced and richer hotel review data collection), and results demonstrate that our approach outperforms previous baselines, showcasing the effectiveness of integrating WSD in ABSA. Furthermore, an ablation study confirms the significant contribution of the WSD module to the overall performance. Moreover, we explore different similarity measures and find that cosine similarity yields the best results when identifying the real sense of a word in a given sentence using WordNet. The findings of our work and future work related to our work create lots of interest for people in the tourism and hospitality industry. This research gives another boost to the concept of the potential of NLP techniques in sentiment analysis. It emphasizes that if we combine the potential of NLP techniques along with state-of-the-art machine learning frameworks, we can shape the future of this field.

Nowadays, people find it easier to express opinions via social media-formally known as Web 2.0. Sentiment analysis is an essential field under natural language processing in Computer Science that deals with analyzing people's opinions on the subject matter and discovering the polarity they contain. These opinions could be processed in collective form (as a document) or segments or units as sentences or phrases. Sentiment analysis can be applied in education, research optimization, politics, business, education, health, science and so on, thus forming massive data that requires efficient tools and techniques for analysis. Furthermore, the standard tools currently used for data collection, such as online surveys, interviews, and student evaluation of teachers, limit respondents in expressing opinions to the researcher's surveys and could not generate huge data as Web 2.0 becomes bigger. Sentiment analysis techniques are classified into three (3): Machine learning algorithms, lexicon and hybrid. This study explores sentiment analysis of Web 2.0 for novice researchers to promote collaboration and suggest the best tools for sentiment data analysis and result efficiency. Studies show that machine learning approaches result in large data sets on document-level sentiment classification. In some studies, hybrid techniques that combine machine learning and lexicon-based performance are better than lexicon. Python and R programming are commonly used tools for sentiment analysis implementation, but SentimentAnalyzer and SentiWordnet are recommended for the novice.
 Keywords: Sentiment Analysis; Web 2.0; Applications; Tools; Novice

Aspect Based Sentiment Analysis (ABSA) is a sentiment analysis technique that not only identifies overall sentiment, but also reveals opinions on specific aspects of an entity. To facilitate computer processing, a numerical representation of words into vectors (word embedding) is used, where each word or phrase is mapped into a vector of dimension N. Although static embedding such as Word2Vec or GloVe has been widely used, these approaches have limitations in capturing the dynamic context essential for deep sentiment analysis. This research develops and tests several deep learning algorithms, namely CNN, Bi-LSTM, CNN+BiLSTM, and CNN+BiLSTM+Attention Mechanism, which initially use static embedding and then modified by integrating BERT as contextual embedding. The results show that the use of BERT improves sentiment prediction accuracy by 15% and aspect prediction accuracy by 11% compared to models with static embedding. In particular, the combination of BERT+CNN obtained the best accuracy, which was 94% for aspect prediction and the combination of BERT+CNN+BiLSTM+Attention Mechanism 87% for sentiment prediction. These findings demonstrate the significant potential of BERT integration in improving ABSA performance, which can be applied in social media opinion analysis and sentiment-based recommendation systems.

Sentiment analysis, a critical task in natural language processing, aims to automatically identify and classify the sentiment expressed in textual data. Aspect-level sentiment analysis focuses on determining sentiment at a more granular level, targeting specific aspects or features within a piece of text. In this paper, we explore various techniques for sentiment analysis, including traditional machine learning approaches and state-of-the-art deep learning models. Additionally, deep learning techniques has been utilized to identifying and extracting specific aspects from text, addressing aspect-level ambiguity, and capturing nuanced sentiments for each aspect. These datasets are valuable for conducting aspect-level sentiment analysis. In this article, we explore a language model based on pre-trained deep neural networks. This model can analyze sequences of text to classify sentiments as positive, negative, or neutral without explicit human labeling. To evaluate these models, data from Twitter's US airlines sentiment database was utilized. Experiments on this dataset reveal that the BERT, RoBERTA and DistilBERT model outperforms than the ML based model in accuracy and is more efficient in terms of training time. Notably, our findings showcase significant advancements over previous state-of-the-art methods that rely on supervised feature learning, bridging existing gaps in sentiment analysis methodologies. Our findings shed light on the advancements and challenges in sentiment analysis, offering insights for future research directions and practical applications in areas such as customer feedback analysis, social media monitoring, and opinion mining.

Due to the rapid increase in User-Generated Content (UGC) data, opinion mining, also called sentiment analysis, has attracted much attention in both academia and industry. Aspect-Based Sentiment Analysis (ABSA), a subfield of sentiment analysis, aims to extract the aspect and the corresponding sentiment simultaneously. Previous works in ABSA may generate undesired aspects, require a large amount of training data, or produce unsatisfactory results. This paper proposes a Graph Neural Network based method to automatically generate aspect-specific sentiment words using a small number of aspect seed words and general sentiment words. It subsequently leverages the aspect-specific sentiment words to improve the Joint Aspect-Sentiment Autoencoder (JASA) model. We conduct experiments on two datasets to verify the proposed model. It shows that our approach has better performance in the ABSA task when compared with previous works.KeywordsAspect-based sentiment analysisAttention mechanismAutoencoderText graph convolution networkText mining

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.

The use of natural language processing and opinion mining methods has been utilized throughout the last couple of years through sentiment analysis to detect, obtain, compute, and examine information which could be valuable to users. Organizations that make use of these methods need these methods to evaluate and improve their customer feedback. There are different types of techniques that have been previously implemented. Sentiment analysis tools can be categorized as machine learning techniques and lexicon-based techniques. The machine learning techniques are divided into supervised and unsupervised learning while lexicon-based techniques are split into dictionary-based and corpus-based approaches. The machine learning techniques categorize the polarity in sentiments while the lexicon-based techniques utilize sentiment lexicons. In this work, different machine learning and lexicon-based techniques have been reviewed to discuss their advantages and their limitations when implemented.

Sentiment analysis, also known as opinion mining, is a computational study of unstructured textual information which is used to analyze a persons attitude from a piece of text. This paper proposes an efficient method for sentiment analysis by effectively combining three procedures: (a) creating the ontologies for extraction of semantic features (b) Word2vec for conversion of processed corpus (c) convolutional neural network (CNN) for opinion mining. For CNN parameter tuning, a multi-objective function is solved for nondominant Pareto front optimal values using particle swarm optimization. Experiments show that the proposed technique outperforms other state-of-the-art techniques while yielding 88.52%, 94.30%, 85.63% and 86.03% in accuracy, precision, recall and F-measure, respectively.

Sentiment Analysis (SA) has been a core interest in the field of text mining research, dealing with computational processing of sentiments, views, and subjective nature of the text. Due to the availability of extensive web-based data in Indian languages such as Hindi, Marathi, Kannada, Tamil, and so on. It has become extremely significant to analyze this data and recover valuable and relevant information. Hindi being the first language of the majority of the population in India, SA in Hindi has turned out to be a critical task particularly for companies and government organizations. This research portrays a systematic review specifically in the field of Hindi SA. The major contribution of this article includes the categorization of numerous articles based on techniques that have attracted researchers in performing SA tasks in Hindi language. This survey classifies these state-of-the-art computational intelligence techniques into four major categories namely lexicon-based techniques, machine learning techniques, deep learning techniques, and hybrid techniques. It discusses the importance of these techniques based on different aspects such as their impact on the issues of SA, levels of analysis, and performance evaluation measures. The research puts forward a comprehensive overview of the majority of the work done in Hindi SA. This study will help researchers in finding out resources such as annotated datasets, linguistic resources, and lexical resources. This survey delivers some significant findings and presents overall future research directions in the field of Hindi SA.

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.

Aspect based sentiment analysis (ABSA) is a fine-grained sentiment analysis task, whose main goal is to identify the sentiment polarity of an aspect in a sentence. A sentence may contain many different aspects, each of which may have different sentiment polarities. Based on the current researches in this area, ABSA can be divided into two subtasks: aspect-category sentiment analysis (ACSA) and aspect-term sentiment analysis (ATSA). In the past, more commonly used method is to adopt the time serial algorithm such as Long Short-Term Memory (LSTM) or Recurrent Neural Network (RNN), which usually needs more training time and has complex structures. Moreover, many previous models lack the abilities to effectively learn the internal structure features of sentences. For ABSA, sometimes the sentence structure may significantly affect the final classification results. However, we found the excellent performance of self-attention algorithm and gating mechanism in some other related researches. Therefore, to solve the problems above, we build a new model based on gating mechanism, combined with convolutional neural networks (CNN) and self-attention mechanism. First, we use self-attention to extract the structural feature of the input, and integrate it with the features of the original sentence extracted by CNN. On such basis, we further combine the aspect-category or aspect-term of the input sentence to form the final sentiment feature. Experiments on SemEval datasets show the performance of our models and the effectiveness of the model is proved.

Reading massive amounts of user-generated text and pulling out the relevant aspects and opinions is a complicated process. Summaries, on the other hand, help busy people who only have a little bit of time to read get the gist of the information quickly. Text summarization is the process of taking the original text and making a shorter version of it that still has all of its informational value and main idea. Humans have a hard time summarizing long texts by hand. Different ways to summarize a text can be put into groups based on the more general techniques of extractive and abstractive summarization. The research paper discusses the need for generating aspect-based summaries and Sentiment analysis. A framework is proposed based on extracting coherent aspects from the reviews and applying the extractive summarization method to generate summaries. In addition, providing insights into the reviews of tourist attractions by using aspect-based sentiment analysis. The results are evaluated using crowdsourcing, Fairsumm, and Centroid method. The crowdsourcing method gives the best result on aspect-based summaries.

Sentiment Analysis is a method to identify, extract, and quantify people's feelings, opinions, or attitudes. The wealth of online data motivates organizations to keep tabs on customers' opinions and feelings by turning to sentiment analysis tasks. Along with the sentiment analysis, the emotion analysis of written reviews is also essential to improve customer satisfaction with restaurant service. Due to the availability of massive online data, various computerized methods are proposed in the literature to decipher text sentiments. The majority of current methods rely on machine learning, which necessitates the pre-training of large datasets and incurs substantial space and time complexity. To address this issue, we propose a novel unsupervised sentiment classification model. This study presents an unsupervised mathematical optimization framework to perform sentiment and emotion analysis of reviews. The proposed model performs two tasks. First, it identifies a review's positive and negative sentiment polarities, and second, it determines customer satisfaction as either satisfactory or unsatisfactory based on a review. The framework consists of two stages. In the first stage, each review's context, rating, and emotion scores are combined to generate performance scores. In the second stage, we apply a non-cooperative game on performance scores and achieve Nash Equilibrium. The output from this step is the deduced sentiment of the review and the customer's satisfaction feedback. The experiments were performed on two restaurant review datasets and achieved state-of-the-art results. We validated and established the significance of the results through statistical analysis. The proposed model is domain and language-independent. The proposed model ensures rational and consistent results.

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.