State of the art: a review of sentiment analysis based on sequential transfer learning

Chan, Jireh Yi-Le; Bea, Khean Thye; Leow, Steven Mun Hong; Phoong, Seuk Wai; Cheng, Wai Khuen

doi:10.1007/s10462-022-10183-8

Cited by 97 publications

(37 citation statements)

References 110 publications

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“…Some researchers also discussed web tools (Zucco et al 2020), fuzzy logic algorithms (Serrano-Guerrero et al 2021), transformer models (Acheampong et al 2021), and sequential transfer learning (Chan et al 2022) for sentiment analysis.…”

Section: Surveys On Methods Of Sentiment Analysismentioning

confidence: 99%

“…Acheampong et al (2021),Ain et al (2017),Alamoodi et al (2021a, b),Asghar et al (2014),Chan et al (2022),Cheng et al (2022), Feldman (2013,Habimana et al (2020),Koto and Adriani (2015),Kumar and Sebastian (2012);Medhat et al (2014),Prabha and Srikanth (2019),Ravi and Ravi (2015),Schouten and Frasincar (2015), Serrano-Guerrero et al (2021), Taboada (2016), Wang et al (2020b), Yue et al (2019), Zhang et al (2018), and Zucco et al (2020) The existing surveys analyzed different methods of sentiment analysis. Sentiment analysis methods based on lexicons, rules, part of speech, term position, statistical techniques, supervised and unsupervised machine learning methods, as well as deep learning methods like LSTM, CNN, RNN, DNN, DBN, BERT and other hybrid approaches have been analyzed and discussed.…”

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confidence: 99%

“…2021;Ain et al 2017;Alamoodi et al 2021b;Asghar et al 2014;Chan et al 2022;Cheng et al 2022;Feldman 2013;Habimana et al 2020;Koto and Adriani 2015; Kumar, Akshi and Sebastian 2012;Medhat et al 2014;Prabha and Srikanth 2019;Ravi and Ravi 2015;Schouten and Frasincar 2015;Serrano-Guerrero et al 2021;Taboada 2016;Wang et al 2020b;Yue et al 2019;Zhang et al 2018; …”

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confidence: 99%

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Survey on sentiment analysis: evolution of research methods and topics

Cui

Wang

et al. 2023

Artif Intell Rev

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Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword cooccurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates keyword co-occurrence analysis with a community detection algorithm. This survey not only compares and analyzes the connections between research methods and topics over the past two decades but also uncovers the hotspots and trends over time, thus providing guidance for researchers. Furthermore, this paper presents broad practical insights into the methods and topics of sentiment analysis, while also identifying technical directions, limitations, and future work.

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Section: Surveys On Methods Of Sentiment Analysismentioning

confidence: 99%

mentioning

confidence: 99%

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Survey on sentiment analysis: evolution of research methods and topics

Cui

Wang

et al. 2023

Artif Intell Rev

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“…Thus, PLMs can be proved to be advantageous to solve similar new tasks using old experience, without training the sentiment analysis model from the scratch. Chan et al (2022) provided a detailed study on the evolution and advancement of sentiment analysis using pretrained models. Additionally, the authors covered various tasks of sentiment analysis, for which the pretrained models can be used.…”

Section: B Unsupervised Learningmentioning

confidence: 99%

Sentiment analysis: A survey on design framework, applications and future scopes

Bordoloi

Biswas

2023

Artif Intell Rev

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Sentiment analysis is a solution that enables the extraction of a summarized opinion or minute sentimental details regarding any topic or context from a voluminous source of data. Even though several research papers address various sentiment analysis methods, implementations, and algorithms, a paper that includes a thorough analysis of the process for developing an efficient sentiment analysis model is highly desirable. Various factors such as extraction of relevant sentimental words, proper classification of sentiments, dataset, data cleansing, etc. heavily influence the performance of a sentiment analysis model. This survey presents a systematic and in-depth knowledge of different techniques, algorithms, and other factors associated with designing an effective sentiment analysis model. The paper performs a critical assessment of different modules of a sentiment analysis framework while discussing various shortcomings associated with the existing methods or systems. The paper proposes potential multidisciplinary application areas of sentiment analysis based on the contents of data and provides prospective research directions.

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“…According to the study [44], existing sentiment classification techniques usually fall into three categories: (1) lexicon-based approach to compound a sentiment dictionary to determine the sentiment polarity of each word, (2) Machine Learning approach to train a sentiment classifier based on the statistical semantic features such as n-gram, Term frequency-inverse document frequency (TF-IDF), bag-of-word, etc., and (3) Deep Learning approach to automatically extract the feature representation via the neural network for sentiment measure. In addition, the study [45] reviewed the sequential transfer learning approaches in analyzing different sentiment-oriented tasks.…”

Section: Sentiment-based Stock Forecasting Approachmentioning

confidence: 99%

A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock Forecasting

et al. 2022

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Stock forecasting is a significant and challenging task. The recent development of web technologies has transformed the communication channel to allow the public to share information over the web such as news, social media contents, etc., thus causing exponential growth of web data. The massively available information might be the key to revealing the financial market’s unexplained variability and facilitating forecasting accuracy. However, this information is usually in unstructured natural language and consists of different inherent meanings. Although a human can easily interpret the inherent messages, it is still complicated to manually process such a massive amount of textual data due to the constraint of time, ability, energy, etc. Due to the different properties of text sources, it is crucial to understand various text processing approaches to optimize forecasting performance. This study attempted to summarize and discuss the current text-based financial forecasting approaches in the aspect of semantic-based, sentiment-based, event-extraction-based, and hybrid approaches. Afterward, the study discussed the strength and weakness of each approach, followed with their comparison and suitable application scenarios. Moreover, this study also highlighted the future research direction in text-based stock forecasting, where the overall discussion is expected to provide insightful analysis for future reference.

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State of the art: a review of sentiment analysis based on sequential transfer learning

Cited by 97 publications

References 110 publications

Survey on sentiment analysis: evolution of research methods and topics

Survey on sentiment analysis: evolution of research methods and topics

Sentiment analysis: A survey on design framework, applications and future scopes

A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock Forecasting

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