Sentiment analysis algorithms and applications: A survey

By Walaa Medhat, A. Hassan, H. Korashy. 2014

3,267 citations recorded by the source corpus.

Abstract Sentiment Analysis (SA) is an ongoing field of research in text mining field. SA is the computational treatment of opinions, sentiments and subjectivity of text. This survey paper tackles a comprehensive overview of the last update in this field. Many recently proposed algorithms' enhancements and various SA applications are investigated and presented briefly in this survey. These articles are categorized according to their contributions in the various SA techniques. The related fields to SA (transfer learning, emotion detection, and building resources) that attracted researchers recently are discussed. The main target of this survey is to give nearly full image of SA techniques and the related fields with brief details. The main contributions of this paper include the sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the sentiment analysis and its related areas.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/8589c0ff39d35630a2798cf6d3f58f63d37b1c03

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In Artificial Intelligence

Citations in the corpus

Sentiment analysis: A review and comparative analysis of web services (2015, background): “Possibly, the most interesting work from the point of view of the SA techniques is [52], which presents a refined categorization of well-known SA techniques (see Fig.”
Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks (2020, methodology): “Basedontermfrequency(tf i )(ascomputed by Equation (2)) and IDF (idf( i )) (as computed by Equation (1)), TF-IDF has been computed based on Equation (3): Taking term frequencies into account, SIF has been computed based on Equation (4), where a is a parameter, which has been generally set to 10 − 4 ”
Approaches, Tools and Applications for Sentiment Analysis Implementation (2015, neutral): “There are three main classification levels [27]:”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications (2015, methodology): “A study of sentiment analyses reported in [138] highlights different”
A survey on classification techniques for opinion mining and sentiment analysis (2017, background): “Several recent surveys and reviews on sentiment analysis consider these levels of opinion mining from this point of view (Medhat et al. 2014; Ravi and Ravi 2015; Balazs and Velasquez 2016; Yan et al. 2017; Sun et al. 2017; Lo et al. 2017).”
Hybrid SDN Networks: A Survey of Existing Approaches (2018, methodology): “A semantic learning algorithm [240] is proposed to infer the legacy device configuration”
A survey of sentiment analysis in social media (2018, background): “Medhat et al. [73] provided a categorization of articles according to the used techniques, which can help the researchers who are familiar with certain techniques to use them in the sentiment analysis and choose the appropriate technique for a certain application.”
The evolution of sentiment analysis - A review of research topics, venues, and top cited papers (2016, background): “In 2014, Medhat, Hassan and Korashy [Medhat et al. 2014] main contribution is the classification and counting of articles using specific approaches related to sentiment analysis.”
Machine Learning-Based Sentiment Analysis for Twitter Accounts (2018, methodology): “Methods of sentiment analysis can be categorized predominantly [1] as machine-learning [2], Lexicon-based [3] and hybrid [4,5].”
Sentiment Analysis Based on Deep Learning: A Comparative Study (2020, background): “y a key role within a majority of these strategies. Figure 5 illustrates a taxonomy of deep-learning-based methods for sentiment analysis. Figure 5. Taxonomy of sentiment analysis techniques. Source: [30,41]. Sentimentanalysis,whetherperformedbymeansofdeeplearningortraditionalmachinelearning, requir”
Few-Shot Learning for Opinion Summarization (2020, background): “…in online resources, such as blogs, reviews, social media, or internet forums, has drawn much attention due to its potential for various information access applications, such as creating digests, search, and report generation (Hu and Liu, 2004; Medhat et al., 2014; Angelidis and Lapata, 2018).”