CineXDrama: Relevance Detection and Sentiment Analysis of Bangla YouTube Comments on Movie-Drama using Transformers: Insights from Interpretability Tool

• URL: http://arxiv.org/abs/2411.06548v1
• Date: Sun, 10 Nov 2024 18:04:41 GMT
• Title: CineXDrama: Relevance Detection and Sentiment Analysis of Bangla YouTube Comments on Movie-Drama using Transformers: Insights from Interpretability Tool
• Authors: Usafa Akther Rifa, Pronay Debnath, Busra Kamal Rafa, Shamaun Safa Hridi, Md. Aminur Rahman,
• Abstract summary: We propose a system that first assesses the relevance of comments and then analyzes the sentiment of those deemed relevant. We introduce a dataset of 14,000 manually collected and preprocessed comments, annotated for relevance (relevant or irrelevant) and sentiment (positive or negative)
• Score: 0.0
• License:
• Abstract: In recent years, YouTube has become the leading platform for Bangla movies and dramas, where viewers express their opinions in comments that convey their sentiments about the content. However, not all comments are relevant for sentiment analysis, necessitating a filtering mechanism. We propose a system that first assesses the relevance of comments and then analyzes the sentiment of those deemed relevant. We introduce a dataset of 14,000 manually collected and preprocessed comments, annotated for relevance (relevant or irrelevant) and sentiment (positive or negative). Eight transformer models, including BanglaBERT, were used for classification tasks, with BanglaBERT achieving the highest accuracy (83.99% for relevance detection and 93.3% for sentiment analysis). The study also integrates LIME to interpret model decisions, enhancing transparency.

Related papers

• You Shall Know a Tool by the Traces it Leaves: The Predictability of Sentiment Analysis Tools [74.98850427240464]
  We show that sentiment analysis tools disagree on the same dataset. We show that the sentiment tool used for sentiment annotation can even be predicted from its outcome.
  arXiv  Detail & Related papers  (2024-10-18T17:27:38Z)
• HOTVCOM: Generating Buzzworthy Comments for Videos [49.39846630199698]
  This study introduces textscHotVCom, the largest Chinese video hot-comment dataset, comprising 94k diverse videos and 137 million comments. We also present the textttComHeat framework, which synergistically integrates visual, auditory, and textual data to generate influential hot-comments on the Chinese video dataset.
  arXiv  Detail & Related papers  (2024-09-23T16:45:13Z)
• Motamot: A Dataset for Revealing the Supremacy of Large Language Models over Transformer Models in Bengali Political Sentiment Analysis [0.0]
  Analyzing political sentiment is critical for understanding the complexities of public opinion processes, especially during election seasons. Our study centers on the creation of the "Motamot" dataset, comprising 7,058 instances annotated with positive and negative sentiments. We evaluate the performance of various PLMs including BanglaBERT, Bangla BERT Base, XLM-RoBERTa, mBERT, and sahajBERT, alongside LLMs such as Gemini 1.5 Pro and GPT 3.5 Turbo.
  arXiv  Detail & Related papers  (2024-07-28T16:34:53Z)
• Detecting Suspicious Commenter Mob Behaviors on YouTube Using Graph2Vec [1.1371889042789218]
  This paper presents a social network analysis-based methodology for detecting suspicious commenter mob-like behaviors among YouTube channels. The method aims to characterize channels based on the level of such behavior and identify com-mon patterns across them. The analysis revealed significant similarities among the channels, shedding light on the prevalence of suspicious commenter behavior.
  arXiv  Detail & Related papers  (2023-11-09T23:49:29Z)
• Analyzing Political Figures in Real-Time: Leveraging YouTube Metadata for Sentiment Analysis [0.0]
  Sentiment analysis using big data from YouTube videos metadata can be conducted to analyze public opinions on various political figures. This study aimed to build a sentiment analysis system leveraging YouTube videos metadata. The sentiment analysis model was built using LSTM algorithm and produces two types of sentiments: positive and negative sentiments.
  arXiv  Detail & Related papers  (2023-09-28T08:15:55Z)
• ViCo: Engaging Video Comment Generation with Human Preference Rewards [68.50351391812723]
  We propose ViCo with three novel designs to tackle the challenges for generating engaging Video Comments. To quantify the engagement of comments, we utilize the number of "likes" each comment receives as a proxy of human preference. To automatically evaluate the engagement of comments, we train a reward model to align its judgment to the above proxy.
  arXiv  Detail & Related papers  (2023-08-22T04:01:01Z)
• Classifying YouTube Comments Based on Sentiment and Type of Sentence [0.0]
  We address the challenge of text extraction and classification from YouTube comments using well-known statistical measures and machine learning models. The results show that our approach that incorporates conventional methods performs well on the classification task, validating its potential in assisting content creators increase viewer engagement on their channel.
  arXiv  Detail & Related papers  (2021-10-31T18:08:10Z)
• SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation [48.1799451277808]
  We propose a Sentiment-aware Interactive Fusion Network (SIFN) for review-based item recommendation. We first encode user/item reviews via BERT and propose a light-weighted sentiment learner to extract semantic features of each review. Then, we propose a sentiment prediction task that guides the sentiment learner to extract sentiment-aware features via explicit sentiment labels.
  arXiv  Detail & Related papers  (2021-08-18T08:04:38Z)
• QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries [89.24431389933703]
  We present the Query-based Video Highlights (QVHighlights) dataset. It consists of over 10,000 YouTube videos, covering a wide range of topics. Each video in the dataset is annotated with: (1) a human-written free-form NL query, (2) relevant moments in the video w.r.t. the query, and (3) five-point scale saliency scores for all query-relevant clips.
  arXiv  Detail & Related papers  (2021-07-20T16:42:58Z)
• Mi YouTube es Su YouTube? Analyzing the Cultures using YouTube Thumbnails of Popular Videos [98.87558262467257]
  This study explores culture preferences among countries using the thumbnails of YouTube trending videos. Experimental results indicate that the users from similar cultures shares interests in watching similar videos on YouTube.
  arXiv  Detail & Related papers  (2020-01-27T20:15:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.