Document Level Sentiment Analysis: A survey

Behdenna, Salima; Barigou, Fatiha; Belalem, Ghalem

doi:10.4108/eai.14-3-2018.154339

Cited by 51 publications

(16 citation statements)

References 15 publications

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“…[ 12 ], or the intensity of agreement based on a numerical rating scale [ 13 ]. SA can be performed at three levels [ 14 ]: (a) document-level SA, performed to find out the global opinion of a document; (b) sentence-level SA, which uncovers the attitude expressed by each sentence; (c) aspect-level SA, which classifies the opinion towards an entity.…”

Section: Introductionmentioning

confidence: 99%

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Vaccine Hesitancy on Social Media: Sentiment Analysis from June 2011 to April 2019

et al. 2021

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Vaccine hesitancy was one of the ten major threats to global health in 2019, according to the World Health Organisation. Nowadays, social media has an important role in the spread of information, misinformation, and disinformation about vaccines. Monitoring vaccine-related conversations on social media could help us to identify the factors that contribute to vaccine confidence in each historical period and geographical area. We used a hybrid approach to perform an opinion-mining analysis on 1,499,227 vaccine-related tweets published on Twitter from 1st June 2011 to 30th April 2019. Our algorithm classified 69.36% of the tweets as neutral, 21.78% as positive, and 8.86% as negative. The percentage of neutral tweets showed a decreasing tendency, while the proportion of positive and negative tweets increased over time. Peaks in positive tweets were observed every April. The proportion of positive tweets was significantly higher in the middle of the week and decreased during weekends. Negative tweets followed the opposite pattern. Among users with ≥2 tweets, 91.83% had a homogeneous polarised discourse. Positive tweets were more prevalent in Switzerland (71.43%). Negative tweets were most common in the Netherlands (15.53%), Canada (11.32%), Japan (10.74%), and the United States (10.49%). Opinion mining is potentially useful to monitor online vaccine-related concerns and adapt vaccine promotion strategies accordingly.

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Section: Introductionmentioning

confidence: 99%

“…There are three types of SA approaches [ 14 ]: lexicon-based approaches, machine-learning approaches, and hybrid approaches—a combination of lexicon and machine-learning approaches. Lexicon-based approaches rely on corpora or dictionaries that contain terms classified by their sentiment [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Vaccine Hesitancy on Social Media: Sentiment Analysis from June 2011 to April 2019

et al. 2021

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“…The analysis of the sentiment, or in general of the opinion, can be performed at different levels of detail, from the most generic to the most specific: document-level, sentence-level, aspect-level, and concept-level (Hemmatian and Sohrabi 2019;Pontiki et al 2016;Nakov et al 2016;Rosenthal et al 2017). The document-level analysis [sometimes referred to as text-level (Pontiki et al 2016)] aims at understanding the polarity of a whole document [e.g., reviews (Behdenna et al 2018), news article (Shirsat et al 2017), a post, a tweet (Gurini et al 2013)]. Therefore, the information is quite general since it summarizes the polarity of many sentences as a unique positive or negative score [usually measured on a two-point or five-point scale (Nakov et al 2016)].…”

Section: Sentiment Analysis: Levels Of Analysismentioning

confidence: 99%

KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis

D’Aniello

Rocca

2022

Artif Intell Rev

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The analysis of the opinions of customers and users has been always of great interest in supporting decision-making in many fields, especially in marketing. Sentiment analysis (SA) is the umbrella term for techniques and approaches that analyze user’s sentiments, emotions, opinions in text or other media. The need for a better understanding of these opinions paved the way to novel approaches that focus on the analysis of the sentiment related to specific features of a product, giving birth to the field of aspect-based sentiment analysis (ABSA). Although the increasing interest in this discipline, there is still confusion regarding the basic concepts of ABSA: terms like sentiment, affect, emotion, opinion, are used as synonyms while they represent different concepts. This often leads to an incorrect analysis of the users’ opinions.This work presents an overview of the state-of-the-art techniques and approaches for ABSA, highlighting the main critical issues related to current trends in this field. Following this analysis, a new reference model for SA and ABSA, namely the KnowMIS-ABSA model, is proposed. The model is grounded on the consideration that sentiment, affect, emotion and opinion are very different concepts and that it is profoundly wrong to use the same metric and the same technique to measure them. Accordingly, we argue that different tools and metrics should be adopted to measure each of the dimensions of an opinion. A qualitative case study, regarding product reviews, is proposed to motivate the advantages of the KnowMIS-ABSA model.

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“…Figure 2 shows the different categories of emotions expressed by the people. Several kinds of research have been carried out in identifying the sentiments expressed by different words and sentences present in documents (Behdenna, Barigou, & Belalem, 2018;Chakraborty, Bhattacharyya, Bag, & Hassanien, 2018;Tang et al, 2016). According to the authors (Asghar, Khan, Ahmad, Qasim, & Khan, 2017), analyzing sentiments from text is mainly based on the lexicon of sentiments and others involve some rules.…”

Section: Textmentioning

confidence: 99%

Multimodal sentimental analysis for social media applications: A comprehensive review

Chandrasekaran

Nguyen

Hemanth

2021

WIREs Data Min & Knowl

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The analysis of sentiments is essential in identifying and classifying opinions regarding a source material that is, a product or service. The analysis of these sentiments finds a variety of applications like product reviews, opinion polls, movie reviews on YouTube, news video analysis, and health care applications including stress and depression analysis. The traditional approach of sentiment analysis which is based on text involves the collection of large textual data and different algorithms to extract the sentiment information from it. But multimodal sentimental analysis provides methods to carry out opinion analysis based on the combination of video, audio, and text which goes a way beyond the conventional text-based sentimental analysis in understanding human behaviors. The remarkable increase in the use of social media provides a large collection of multimodal data that reflects the user's sentiment on certain aspects. This multimodal sentimental analysis approach helps in classifying the polarity (positive, negative, and neutral) of the individual sentiments. Our work aims to present a survey of recent developments in analyzing the multimodal sentiments (involving text, audio, and video/image) which involve humanmachine interaction and challenges involved in analyzing them. A detailed survey on sentimental dataset, feature extraction algorithms, data fusion methods, and efficiency of different classification techniques are presented in this work.

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Document Level Sentiment Analysis: A survey

Cited by 51 publications

References 15 publications

Vaccine Hesitancy on Social Media: Sentiment Analysis from June 2011 to April 2019

Vaccine Hesitancy on Social Media: Sentiment Analysis from June 2011 to April 2019

KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis

Multimodal sentimental analysis for social media applications: A comprehensive review

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