Lexicon and Heuristics Based Approach for Identification of Emotion in Text

Akram, Junaid; Tahir, Arsalan

doi:10.1109/fit.2018.00058

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Of course, the modalities obtained are different from the features that can be analyzed. Speech signals are more feature-rich than conversation transcripts (text [166]). Various information such as prosodic, spectral, voice quality, and features based on Teager energy operator can be analyzed from speech signals [167].…”

Section: Research Topic In Multimodal Emotion Recognitionmentioning

confidence: 99%

A Systematic Literature Review of Multimodal Emotion Recognition

Rahayu,

Muharrom,

Windiarti

et al. 2023

SJI

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Purpose: This literature review aims to identify Multimodal Emotion Recognition (MER) in depth and breadth by analysing the topics, trends, modalities, and other supporting sources discussed in research over the years and between 2010 and 2022. Based on the screening analysis, a total of 14,533 articles were analysed to achieve this goal.Methods: This research was conducted in 3 (three) phases, including Planning, Conducting and Reporting. The first step was defining the research objectives by searching for systematic reviews with similar topics to this study, then reviewing them to develop research questions and systematic review protocols for this study. The second stage is to collect articles according to a pre-determined protocol, selecting the articles obtained and then conducting an analysis of the filtered articles in order to answer the research questions. The final stage is to summarize the results of the analysis so new findings from this research can be reported. Result: In general, the focus of MER research can be categorised into two issues, namely the object background and the source or modality of emotion recognition. When looking at the object background, most of the 55% to support emotion recognition with a health background, especially brain function decline, 34% based on age, 10% based on gender, 1% data collection situation and a small portion of less than 1% related to ethnic culture. In terms of the source of emotion recognition, research is divided into electromagnetic signals, voice signals, text, photo/video and the development of wearable devices. Based on the above results, there are at least 7 scientific fields that discuss MER research, namely health, psychology, electronics, grammar, communication, socio-culture and computer science.Novelty: MER research has the potential to develop further. There are still many areas that have received less attention, while the ecosystem that uses them has grown massively. Emotion recognition modalities are numerous and diverse, but research is still focused on validating the emotions of each modality, rather than exploring the strengths of each modality to improve the quality of recognition results.

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Section: Research Topic In Multimodal Emotion Recognitionmentioning

confidence: 99%

A Systematic Literature Review of Multimodal Emotion Recognition

Rahayu,

Muharrom,

Windiarti

et al. 2023

SJI

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“…While some have reported comparable results to our study’s lexicon analysis, they have not demonstrated quality control to ensure the validity of Twitter as a reliable data source. For example, Akram & Tahir (2019) collected emoticons from Twitter to identify emotions through lexicons, but their study lacked expert evaluation and only focused on emoticons instead of the entire tweet.…”

Section: Background and State Of The Artmentioning

confidence: 99%

Using social media and personality traits to assess software developers’ emotional polarity

Silva,

Gurgel de Castro,

Bernardino Silva

et al. 2023

PeerJ Computer Science

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Although human factors (e.g., cognitive functions, behaviors and skills, human error models, etc.) are key elements to improve software development productivity and quality, the role of software developers’ emotions and their personality traits in software engineering still needs to be studied. A major difficulty is in assessing developers’ emotions, leading to the classic problem of having difficulties understanding what cannot be easily measured. Existing approaches to infer emotions, such as facial expressions, self-assessed surveys, and biometric sensors, imply considerable intrusiveness on developers and tend to be used only during normal working periods. This article proposes to assess the feasibility of using social media posts (e.g., developers’ posts on Twitter) to accurately determine the polarity of emotions of software developers over extended periods in a non-intrusive manner, allowing the identification of potentially abnormal periods of negative or positive sentiments of developers that may affect software development productivity or software quality. Our results suggested that Twitter data can serve as a valid source for accurately inferring the polarity of emotions. We evaluated 31 combinations of unsupervised lexicon-based techniques using a dataset with 79,029 public posts from Twitter from sixteen software developers, achieving a macro F1-Score of 0.745 and 76.8% of accuracy with the ensemble comprised of SentiStrength, Sentilex-PT, and LIWC2015_PT lexicons. Among other results, we found a statistically significant difference in tweets’ polarities posted during working and non-working periods for 31.25% of the participants, suggesting that emotional polarity monitoring outside working hours could also be relevant. We also assessed the Big Five personality traits of the developers and preliminarily used them to ponder the polarities inferences. In this context, Openness, Conscientiousness, and Extraversion were frequently related to neutral and positive posts, while Neuroticism is associated with negative posts. Our results show that the proposed approach is accurate enough to constitute a simple and non-intrusive alternative to existing methods. Tools using this approach can be applied in real software development environments to support software team workers in making decisions to improve the software development process.

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“…[28] used domain-specific emotion lexicons (DSLs) and general-purpose emotion lexicons (GPELs) to study feature extraction emotional problems. [29] used the Lexicon approach to generate emotional weights or values. Before a specified model recognizes the emotion in a text, the text must have a particular stage so the machine can understand the series of words or sentences.…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction using Lexicon on the Emotion Recognition Dataset of Indonesian Text

Nurkasanah

Hayaty

2022

Ultimatics

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Text Mining is a part of Neural Language Processing (NLP), also known as text analytics. Text mining includes sentiment analysis and emotion analysis which are often used in analysis on social media, news, or other media in written form. The emotional breakdown is a level of sentiment analysis that categorises text into negative, neutral, and positive sentiments. Emotion is categorized into several classes, In this study, emotion is categorized into 5 classes namely anger, fear, happiness, love, and sadness. This study proposed feature extraction using Lexicon and TF-IDF on the emotion recognition dataset of Indonesian texts. InSet Lexicon Dictionary is used as the corpus in performing the feature extraction. Therefore, InSet Lexicon was chosen as the dictionary to perform feature extraction in this study. The results show that InSet Lexicon has poor performance in feature extraction by showing an accuracy of 30%, while TF-IDF is 62%.

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Lexicon and Heuristics Based Approach for Identification of Emotion in Text

Cited by 5 publications

References 18 publications

A Systematic Literature Review of Multimodal Emotion Recognition

A Systematic Literature Review of Multimodal Emotion Recognition

Using social media and personality traits to assess software developers’ emotional polarity

Feature Extraction using Lexicon on the Emotion Recognition Dataset of Indonesian Text

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