LexTex: a framework to generate lexicons using WordNet word senses in domain specific categories

Dessı̀, Danilo; Recupero, Diego Reforgiato

doi:10.1007/s10844-021-00679-0

Cited by 2 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Techniques based on SentiWordNet, WordNet and ML suffer from several challenges including design complexities due to data dimentionality because of data sparsity (Miranda et al, 2019;Asgarian et al, 2018;Khan et al, 2017;Dehkharghani et al, 2016). Techniques based on MLs and hybrid MLs (Dessì et al, 2021;Qureshi and Sabih, 2021;Baydogan and Alatas, 2021;Kapil and Ekbal, 2020;Mossie and Wang, 2020), among other shortcomings include their inability to capture underlying semantics of words.…”

Section: Problem Statement and Motivationmentioning

confidence: 99%

See 1 more Smart Citation

An Optimal WordNet Based Emotional Word Extraction and Hybrid Deep Learning Classifier for Sentiment Analysis

Obare,

Ade-Ibijola,

Ogada

2023

IJDSBDA

View full text Add to dashboard Cite

Everyday, over one billion social media text messages are generated worldwide, which is a rich source of data that can lead to improvements in the lives of citizens through evidence-based decision making. Twitter is rich in such data but there are number of challenges in processing tweets with respect to volume, speed, ambiguity of the language in which tweets are written, which is an information extraction problem, in the domain of Natural Language Processing (NLP). While there is a growing interest in sentiment analysis for detecting emotions from tweets, there are no major efforts for detecting emotions that are disguised in tweets based on context of word usage, which is important for tasks such as identification of events such as hate speech, mental health related disorders. This paper presents a novel approach to context-based hate speech detection based on an optimal WordNet. Taking a modified Mayfly Optimization algorithm (MMO), we pre-process tweets and normalize the data using an NLP pipeline. We argument an improved Horse Herd Optimization algorithm (HHO) with WordNet and SentiWordNet to compute a tweet sentiment polarity. A hybrid Deep Belief Artificial Neural Network (hybrid DB-ANN) is then used to classify tweets. The performance of the proposed approach is compared to best known sentiment analysis algorithms using three standard benchmark datasets: Crowdflower-1, Crowdflower-2, and Kaggle Twitter. We demonstrate that the proposed approach outperforms industry benchmarks as it achieves over 90% in terms of accuracy, precision, recall, and F-measure.

show abstract

Section: Problem Statement and Motivationmentioning

confidence: 99%

“…A system was developed to create lexicons irrespective of the target domain and input classes (Dessì and Diego, 2021). They employed existing natural language processing, WordNet disambiguation tools, and methodologies.…”

Section: Introductionmentioning

confidence: 99%

An Optimal WordNet Based Emotional Word Extraction and Hybrid Deep Learning Classifier for Sentiment Analysis

Obare,

Ade-Ibijola,

Ogada

2023

IJDSBDA

View full text Add to dashboard Cite

show abstract

“…Similar to this, it may be more di cult to discern the correct word senses in shorter input texts, but we would likely achieve greater precision in scoring. (Dessì, 2021) Social media posts are prominent and frequently loaded with emotion. By analyzing and examining these social media posts, it may be possible to identify emotional states and their underlying causes.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of tweets on Indian movies performance: An integrative approach using sentiment analysis and emotion detection

Sarin,

Agarwal,

Goyal

2023

Preprint

View full text Add to dashboard Cite

Due to social media's rapid growth, moviegoers primarily rely on internet reviews before buying. Studios must determine how reviews affect sales. This study examines how online reviews affect movie box office earnings using sentiment analysis and emotion recognition. Text reviews affect attendance and revenue as much as rating does. This data can help filmmakers and advertising target demographics. Films met audience expectations, with increased trust and happiness and lower wrath, surprise, disgust, and sadness. However, some films scored better for specific emotions, suggesting they elicited different audience responses. Most film tweets were happier and more trusting after release than before. However, tweets about some films showed more wrath and dread, suggesting marketing or audience reception concerns.

show abstract

LexTex: a framework to generate lexicons using WordNet word senses in domain specific categories

Cited by 2 publications

References 39 publications

An Optimal WordNet Based Emotional Word Extraction and Hybrid Deep Learning Classifier for Sentiment Analysis

An Optimal WordNet Based Emotional Word Extraction and Hybrid Deep Learning Classifier for Sentiment Analysis

Impact of tweets on Indian movies performance: An integrative approach using sentiment analysis and emotion detection

Contact Info

Product

Resources

About