Emotional Variance Analysis: A new sentiment analysis feature set for Artificial Intelligence and Machine Learning applications

Tan, Leonard; Tan, Ooi Kiang; Sze, Chun Chau; Goh, Wilson Wen Bin

doi:10.1371/journal.pone.0274299

Cited by 8 publications

(3 citation statements)

References 32 publications

(34 reference statements)

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“…In CIDER, we calculate an ‘intensity’ metric alongside the standard ‘pos’, ‘neg’, ‘neu’, and ‘compound’ VADER scores. Similar to the Emotional Variance Analysis (EVA) tool [ 35 ], which focuses on the variance of emotional expressions in texts, CIDER’s approach also acknowledges the complexity of emotions beyond simple polarity. However, unlike EVA which calculates variance, CIDER derives intensity by first applying VADER’s default mutation rules for boosting and negation to calculate word-level polarity scores, and then taking the absolute values of these scores.…”

Section: Methodsmentioning

confidence: 99%

“…These were then filtered to keep only tweets in the UK. Whilst this is only a sample of the true volume of tweets from the UK (typically geotagged tweets consist of *1% of total tweet volume [9]), the high volume of tweets (35,990,879 tweets) provided a sufficient overview of tweets from the UK. Due to data collection outages, only 318 days of tweets are present in the dataset.…”

Section: Weather Tweetsmentioning

confidence: 99%

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CIDER: Context-sensitive polarity measurement for short-form text

Young,

Arthur,

Williams

2024

PLoS ONE

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Researchers commonly perform sentiment analysis on large collections of short texts like tweets, Reddit posts or newspaper headlines that are all focused on a specific topic, theme or event. Usually, general-purpose sentiment analysis methods are used. These perform well on average but miss the variation in meaning that happens across different contexts, for example, the word “active” has a very different intention and valence in the phrase “active lifestyle” versus “active volcano”. This work presents a new approach, CIDER (Context Informed Dictionary and sEmantic Reasoner), which performs context-sensitive linguistic analysis, where the valence of sentiment-laden terms is inferred from the whole corpus before being used to score the individual texts. In this paper, we detail the CIDER algorithm and demonstrate that it outperforms state-of-the-art generalist unsupervised sentiment analysis techniques on a large collection of tweets about the weather. CIDER is also applicable to alternative (non-sentiment) linguistic scales. A case study on gender in the UK is presented, with the identification of highly gendered and sentiment-laden days. We have made our implementation of CIDER available as a Python package: https://pypi.org/project/ciderpolarity/.

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Section: Methodsmentioning

confidence: 99%

Section: Weather Tweetsmentioning

confidence: 99%

CIDER: Context-sensitive polarity measurement for short-form text

Young,

Arthur,

Williams

2024

PLoS ONE

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“…Tan et al [21] presented EVA, a novel SA feature for identifying text emotional instability. This work has the potential to be applied in the fields of mental health as well as consumer behavior.…”

Section: Literature Reviewmentioning

confidence: 99%

Higher Educational Institution (HEI) Promotional Management Support System Through Sentiment Analysis for Student Intake Improvement

et al. 2023

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Promotional activities with Emotional Intelligence (EI) are more effective than factual information. It is significant in Higher Educational Institutions (HEIs) marketing and reputation management. Choosing an HEI is a crucial decision. Factual information ignites the human brain's analytical characteristics, making logical decisions while choosing an HEI. Promotional activities that target the emotional states push the target group to overlook logic and make emotional decisions. This paper presents a novel HEI Promotional Management Support (PMS) system that utilizes this phenomenon through sentiment analysis to enhance marketing capabilities. A Bi-Directional-Long-Short-Term-Memory (BiLSTM) network has been designed and implemented in this paper to classify the existing students' sentiments into four classes. It classifies the sentiment with an average accuracy of 92.75%. The positive sentiment of the students is used to target the promotional content, whereas negative sentiment guides to avoid content that has the probability of raising negative concerns. The application of this novel PMS system demonstrates a 4.78% improvement in student intake over an observational period of 24 months. The unique concept, effective implementation, and remarkable result indicate that the proposed HEI Promotional Management Support System is a revolutionary application of Artificial Intelligence (AI) to improve student intake rate.

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Mediating effects of NLP-based parameters on the readability of crowdsourced wikipedia articles

Setia,

Chhabra,

Arjun Verma

et al. 2024

Appl Intell

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In this era of information and communication technology, a large population relies on the Internet to gather information. One of the most popular information sources on the Internet is Wikipedia. Wikipedia is a free encyclopedia that provides a wide range of information to its users. However, there have been concerns about the readability of information on Wikipedia time and again. The readability of the text is defined as the ease of understanding the underlying text. Past studies have analyzed the readability of Wikipedia articles with the help of conventional readability metrics, such as the Flesch-Kincaid readability score and the Automatic Readability Index (ARI). Such metrics only consider the surface-level parameters, such as the number of words, sentences, and paragraphs in the text, to quantify the readability. However, the readability of the text must also take into account the quality of the text. In this study, we consider many new NLP-based parameters capturing the quality of the text, such as lexical diversity, semantic diversity, lexical complexity, and semantic complexity and analyze their impact on the readability of Wikipedia articles using artificial neural networks. Besides NLP parameters, the crowdsourced parameters also affect the readability, and therefore, we also analyze the impact of crowdsourced parameters and observe that the crowdsourced parameters not only influence the readability scores but also affect the NLP parameters of the text. Additionally, we investigate the mediating effect of NLP parameters that connect the crowdsourced parameters to the readability of the text. The results show that the impact of crowdsourced parameters on readability is partially due to the profound effect of NLP-based parameters.

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Emotional Variance Analysis: A new sentiment analysis feature set for Artificial Intelligence and Machine Learning applications

Cited by 8 publications

References 32 publications

CIDER: Context-sensitive polarity measurement for short-form text

CIDER: Context-sensitive polarity measurement for short-form text

Higher Educational Institution (HEI) Promotional Management Support System Through Sentiment Analysis for Student Intake Improvement

Mediating effects of NLP-based parameters on the readability of crowdsourced wikipedia articles

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