Extraction2020720phindienglishvegamoviesn Hot

The exponential growth of user-generated content on streaming platforms and social media has led to a surge in code-mixed text, particularly Hindi-English (Hinglish). Extracting meaningful keyphrases from such unstructured data remains challenging due to lexical variations, lack of standardized grammar, and resource scarcity. This paper proposes a hybrid keyphrase extraction model combining statistical features (TF-IDF, TextRank) with a lightweight neural sequence labeler. Evaluated on a manually annotated corpus of 5,000 movie review sentences from online forums, the proposed model achieves an F1-score of 0.74, outperforming baseline methods by 12%. The approach demonstrates robust performance on named entities, movie titles, and sentiment-bearing phrases.

The extraction and analysis reveal a growing interest in accessible, categorized movie databases. For viewers interested in Hindi and English cinema, these platforms offer a convenient way to explore content. However, challenges such as content rights, regional limitations, and user preferences continue to pose challenges. extraction2020720phindienglishvegamoviesn hot

We scraped 5,000 user comments from movie discussion forums (excluding any pirated sources, focusing on public review data). Two bilingual annotators labeled keyphrases with high inter-annotator agreement (Cohen’s kappa = 0.81). Evaluated on a manually annotated corpus of 5,000

The rise of digital platforms has revolutionized the way we consume media, including movies. Platforms focusing on specific genres or types of content, such as Vegamovies, have gained popularity. However, there's limited information on what such platforms offer, especially concerning specific language preferences like Hindi and English. For viewers interested in Hindi and English cinema,

Keyphrase extraction is a fundamental task in information retrieval and natural language processing (NLP). It enables efficient summarization, indexing, and recommendation. However, most existing systems assume monolingual, well-structured text. In contrast, Indian language speakers frequently switch between Hindi (written in Roman script) and English, creating code-mixed text. For example: "Ye movie bahut awesome hai, must watch."

Platforms discussing movies (e.g., VegaMovies, YouTube comments, Telegram groups) are rich sources of such code-mixed data. Extracting keyphrases like "must watch", "bahut awesome", or movie names can improve content moderation and recommendation systems.