Sentiment-Oriented Information Retrieval: Affective Analysis of Documents Based on the SenticNet Framework

Bisio, Federica; Meda, Claudia; Gastaldo, Paolo; Zunino, Rodolfo; Wang, Zhaoxia

doi:10.1007/978-3-319-30319-2_8

Cited by 5 publications

(5 citation statements)

References 52 publications

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“…[96] Sentiment analysis can be done using two main automatic learning approaches: supervised and unsupervised. Unsupervised approaches (also called lexiconbased method) are based on sentiment lexicons like dictionaries [117,25,91,40,55]. The lexicon-based approach relies on a sentiment lexicon containing a set of words with their valences (positive/ negative/ neutral).…”

Section: Sentiment Analysis: An Overviewmentioning

confidence: 99%

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ArAutoSenti: automatic annotation and new tendencies for sentiment classification of Arabic messages

Guellil

Azouaou

Chiclana

2020

Soc. Netw. Anal. Min.

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A corpus-based sentiment analysis approach for messages written in Arabic and its dialects is presented and implemented. The originality of this approach resides in the automation construction of the annotated sentiment corpus, which relies mainly on a sentiment lexicon that is also constructed automatically. For the classification step, shallow and deep classifiers are used with features being extracted applying word embedding models. For the validation of the constructed corpus, we proceed with a manual reviewing and it was found that 85.17% were correctly annotated. This approach is applied on the under-resourced Algerian dialect and the approach is tested on two external test corpora presented in the literature. The obtained results are very encouraging with an F1-score that is up to 88% (on the first test corpus) and up to 81% (on the second test corpus). These results respectively represent a 20% and a 6% improvement, respectively, when compared with existing work in the research literature.

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Section: Sentiment Analysis: An Overviewmentioning

confidence: 99%

“…SenticNet contains more than 5,700 polarity concepts. SenticNet was used in different research works including [100,101,25].…”

Section: Sentiment Analysis In Other Languagesmentioning

confidence: 99%

ArAutoSenti: automatic annotation and new tendencies for sentiment classification of Arabic messages

Guellil

Azouaou

Chiclana

2020

Soc. Netw. Anal. Min.

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“…[9] discussed the importance of aggregating semantics in IR models. [10] investigated the distribution of the emotions within textbooks, which resulted in the development of a framework for sentiment classification (SenticNet). [11] proposed a sentiment-aware attention method which leverages a three-step strategy to boost the performance sentiment analysis regarding movie reviews.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Opinion-Aware Retrieval Models Based on Sentiment and Intensity of Lexical Features

Bahrani¹,

Roelleke²

2021

Frontiers in Artificial Intelligence and Applications

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Sentiment analysis has received much attention in Information Retrieval (IR) and other domains including data mining, machine learning algorithms and NLP. However, when it comes to big data, incorporating sentiment of words into IR models becomes even more important, and as yet no widely accepted standard exists for this task. The contribution of this paper is a framework for quantifying term frequency (TF) variants with sentiments. We propose models derived from the strength of lexical features to improve sentiment-based ranking.

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“…Sentiment analysis is mainly used for data mining and analytics, particularly in judging human behavior [1][2]. It is also used to extract meaningful insights among people's opinions and feelings [3], [4]. Sentiment analysis also provides shared results of thoughts of the masses based on the enormous text data that can be found and are readily available over the internet [5], [6], [7], [35].…”

Section: Introductionmentioning

confidence: 99%

“…Sentiment analysis also provides shared results of thoughts of the masses based on the enormous text data that can be found and are readily available over the internet [5], [6], [7], [35]. Multiple studies have used sentiment analysis to discover important context within a collection of social discourse [5], [8], [9], [10], as a tool to retrieve and extract contents and features relating to specific topics [4] [11], [12]. Different social networking platforms is used and has a huge potential as a source for retrieving vast amounts of information [13][14] are mostly unorganized [15], [34], [35] and in particular, it can be used to determine the opinion of the public on a variety of subjects [9].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis: A Case Study among the selected Government Agencies in the Philippines

M.¹

2020

IJATCSE

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This paper describes a sentiment analysis method to analyze comments polarity and to facilitate selected government agencies such as DepEd, DPWH, DILG, DND, DSWD, and DOH to describe quantitatively the opinions and comments of active users and citizens on social networks such as YouTube. The method used was from the latest comments from the first 100 were selected from each video to form the final datasets used in the study. The comments were extracted from 1450 videos and contain a total number of 14,506 posts. The objectives of the study were (1) to analyze the citizen's attitude among the six government agencies in the category of positive and negative polarity. (2) to utilized the results in developing a decision support system.

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Sentiment-Oriented Information Retrieval: Affective Analysis of Documents Based on the SenticNet Framework

Cited by 5 publications

References 52 publications

ArAutoSenti: automatic annotation and new tendencies for sentiment classification of Arabic messages

ArAutoSenti: automatic annotation and new tendencies for sentiment classification of Arabic messages

Opinion-Aware Retrieval Models Based on Sentiment and Intensity of Lexical Features

Sentiment Analysis: A Case Study among the selected Government Agencies in the Philippines

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