Using Opinionated-Objective Terms to Improve Lexicon-Based Sentiment Analysis

Ogunleye, Bayode; Brunsdon, Teresa; Maswera, Tonderai; Hirsch, Laurence; Gaudoin, Jotham

doi:10.1007/978-981-97-3292-0_1

Cited by 1 publication

(2 citation statements)

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“…Sentiment analysis is the process of classifying text into sentiment categories such as positive, negative [16,17]. There are two popular approaches to perform sentiment analysis namely, lexiconbased approach and ML based approach.…”

Section: Sentiment Analysismentioning

confidence: 99%

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Sentiment Informed Sentence BERT-Ensemble Algorithm for Depression Detection

Ogunleye,

Sharma,

Shobayo

2024

Preprint

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The world health organisation (WHO) revealed approximately 280 million people in the world suffer from depression. Yet, existing studies on early-stage depression detection using machine learning (ML) techniques are limited. Prior studies have applied a single stand-alone algorithm which are unable to deal with data complexities, prone to overfitting and limited in generalisation. To this end, our paper examined the performance of several ML algorithms for early-stage depression detection using two benchmark social media datasets (D1 and D2). More specifically, we incorporated sentiment indicator to improve our model performance. Our experimental results showed that sentence bidirectional encoder representations from transformers (SBERT) numerical vectors fitted into stacking ensemble model achieved comparable F1 scores of 69% in dataset (D1) and 76% in dataset (D2). Our findings suggest that utilising sentiment indicators as additional feature for depression detection yields an improved model performance and thus, we recommend the development of depressive term corpus for future work.

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Section: Sentiment Analysismentioning

confidence: 99%

“…People obtain information or express their feelings freely on social media [1,16,17]. Thus, user generated contents (UGC) are considered a reliable data source for developing automated depression detection model [18,19].…”

Section: Introductionmentioning

confidence: 99%