Paper Title
Predicting Psychological State from Instagram Profile Data using Sentiment Analysis and Emotion Classification
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Abstract
In this In the social media age, it is more crucial than ever to comprehend consumers' psychological states. This study uses profile data from Instagram users to present a novel approach for predicting their psychological states. Using the Apify API, user profiles are scraped in order to obtain post photos, captions, and comments. Next, a pre-trained Hugging Face model is used to perform sentiment analysis on the comments; the findings are color-coded for simple understanding. The post photographs are simultaneously subjected to an emotion classifier that divides them into groups according to emotions including happiness, sorrow, rage, surprise, disgust, fear, and neutrality. A pie chart that shows the frequency of each emotion gives a clear picture of the user's emotional state as it is exhibited through their Instagram activity. The most often identified emotion is then used to forecast the user's psychological state. This novel method has demonstrated promise in offering insightful information on user psychology, with implications for tracking mental health and delivering tailored content. Nevertheless, additional investigation is necessary to corroborate these results using a more extensive sample size and a wider range of user demographics, consequently augmenting the resilience and relevance of this approach.
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How To Cite (APA)
Prajwal.H, Nithya Prasasthani , Fathima Ayana, Akhil Syed, & Dr. N P Nethravathi (May-2024). Predicting Psychological State from Instagram Profile Data using Sentiment Analysis and Emotion Classification. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), c29-c34. https://ijnrd.org/papers/IJNRD2405204.pdf
Issue
Volume 9 Issue 5, May-2024
Pages : c29-c34
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Paper Reg. ID: IJNRD_220942
Published Paper Id: IJNRD2405204
Downloads: 000122257
Research Area: Engineering
Author Type: Indian Author
Country: Doddaballapur, Karnatake, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2405204.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405204
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