Emotion Classification and Crowd Source Sensing; A Lexicon Based Approach

Kamal, Rashid; Shah, Munam Ali; Maple, Carsten; Masood, Mohsin; Wahid, Abdul; Mehmood, Amjad

doi:10.1109/access.2019.2892624

Cited by 21 publications

(8 citation statements)

References 47 publications

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“…Vasileios Lampos et al [5] have used an approach of analyzing the t witter data to monitor the diffusion of pandemic in people. Rashid Kamal et al [6] proposed a crowd source sensing technique using twitter data and word model which can classify the emotions and proved to be more efficient when analysis is done based on location. In many twitter dataset available, location fro m which tweet is sent is not mentioned.…”

Section: Literature Reviewmentioning

confidence: 99%

Emotional Analysis using Twitter Data during Pandemic Situation: COVID-19

Mathur

Kubde

Vaidya

2020

2020 5th International Conference on Communication and Electronics Systems (ICCES)

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During the crisis situation caused due to COVID-19 disease, managing mental health and psychological well-being is as important as physical health of people. As web based life is broadly utilized by individuals to communicate their feeling and supposition, our framework utilizes Twitter information posted by individuals during this emergency circumstance to dissect the feelings of individuals. For processing the cleaned data NRC Word-Emotion Association Lexicon (have aka EmoLex) is used. NRC Word-Emotion Association Lexicon is a list of English with real-valued scores of intensity for eight basic emotion words ns (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust). The text content of tweeter dataset created by fetching tweets across the world have classified into basic emotions like anger, anticipation, disgust, fear, joy, sadness, surprise and trust. This analysis can be used by authorities to understand the mental health of the people and can take necessary measures to decide on policies to fight against coronavirus which is affecting the social well-being as well as economy of the whole world.

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Section: Literature Reviewmentioning

confidence: 99%

Emotional Analysis using Twitter Data during Pandemic Situation: COVID-19

Mathur

Kubde

Vaidya

2020

2020 5th International Conference on Communication and Electronics Systems (ICCES)

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“…-Ex3: "avlgee kannada baralla avl enu kannada kalstale" -Translation: "She doesn't know Kannada how she can teach Kannada" -Google Translation: "What is Kannada?" To address the above limitations, few researchers are trying to use the lexicon-based approach for emotion prediction [14]. Here, the lexicon-based approach does not require any training data and these are computationally not expensive in comparison with ML and DL.…”

Section: Figure 1 Different Approaches For Emotion Predictionmentioning

confidence: 99%

NBLex: emotion prediction in Kannada-English code-switch text using naïve bayes lexicon approach

Chundi

Hulipalled

Simha

2023

IJECE

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<span lang="EN-US">Emotion analysis is a process of identifying the human emotions derived from the various data sources. Emotions can be expressed either in monolingual text or code-switch text. Emotion prediction can be performed through machine learning (ML), or deep learning (DL), or lexicon-based approach. ML and DL approaches are computationally expensive and require training data. Whereas, the lexicon-based approach does not require any training data and it takes very less time to predict the emotions in comparison with ML and DL. In this paper, we proposed a lexicon-based method called non-binding lower extremity exoskeleton (NBLex) to predict the emotions associated with Kannada-English code-switch text that no one has addressed till now. We applied the One-vs-Rest approach to generate the scores for lexicon and also to predict the emotions from the code-switch text. The accuracy of the proposed model NBLex (87.9%) is better than naïve bayes (NB) (85.8%) and bidirectional long short-term memory neural network (BiLSTM) (84.7%) and for true positive rate (TPR), the NBLex (50.6%) is better than NB (37.0%) and BiLSTM (42.2%). From our approach, it is observed that a simple additive model (lexicon approach) can also be an alternative model to predict the emotions in code-switch text.</span>

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“…The research was conducted on 18,659 advertisement tweets by three major cell phone companies selling their phones in India, the results of which revealed that Xiaomi appears, in every aspect, to be the winner of all the three brand names -Xiaomi, Honor, and OnePlus, irrespective of the scope of SM. In the same year, a study by Fernández-Gavilanes, Juncal-Martínez, García-Méndez, Costa-Montenegro, & González-Castaño proposed the combination of the position prediction approach with an unmonitored SA strategy to assess, automatically, the presence in various countries of social network users during an event of global impact [15]. A study was done to examine users' or potential consumer preferences to evaluate users' emotional responses to different types of posts (images or video) distributed through SM channels.…”

Section: Data Presentationmentioning

confidence: 99%

Use of sentiment analysis in social media campaign design and analysis

Gupta¹,

Sandhane²

2022

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Sentiment Analysis is the process by which opinions are defined and categorized as positive, neutral, or negative in a given piece of text. It is a vital part of every strategy for tracking social media. It helps to understand what someone is thinking behind a social media post. Knowing the emotion will provide valuable background for the businesses to move further and react. Over the last decade, numerous studies have been carried out on how businesses can use Sentiment Analysis to understand and quantify the feelings of their consumers about their brand, products, and services. An in-depth analysis of the existing empirical literature on SA regarding social media campaigns is provided using a systematic review of the literature. This paper presents the findings according to the preferred reporting items for systematic reviews and meta-analysis (PRISMA). The aim is to create a resource of the methods and techniques used in the studies to assist both researchers and academia. We subsequently identified the corpus trends, challenges, and implications. We suggested avenues for further research in the application of SA in social media campaign design and analysis.

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Emotion Classification and Crowd Source Sensing; A Lexicon Based Approach

Cited by 21 publications

References 47 publications

Emotional Analysis using Twitter Data during Pandemic Situation: COVID-19

Emotional Analysis using Twitter Data during Pandemic Situation: COVID-19

NBLex: emotion prediction in Kannada-English code-switch text using naïve bayes lexicon approach

Use of sentiment analysis in social media campaign design and analysis

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