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首页> 外文会议>2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques >Cross-lingual sentiment analysis for Indian regional languages
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Cross-lingual sentiment analysis for Indian regional languages

机译：印度地区语言的跨语言情感分析

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摘要

The process of identifying the opinion and categorization of a given dataset to determine whether the attitude of a writer towards the given data is positive, negative or neutral is called as Sentiment Analysis. Sentiment Analysis involves computational identification of opinion on given dataset. It is also referred as the extraction of the opinion. Cross lingual Sentiment Analysis refers to the generation of the opinions in two languages. One language is highly rich in its resources providing sufficient dataset required for the opinion extraction known as Resource Rich Language. The other languages such as Kannada, Hindi, Marati which are poor in its resources and lacks in the data Wordnet and seeks the help of resource rich languages for the opinion extraction known as Resource Poor Language. Sentiment Analysis may be the opinions of movie reviews and social media responses. Here we have used architecture of auto encoder which helps in the generation of the sentiment analysis in two languages. Sentiment Analysis of two languages can be performed by using the Bilingually Constrained Recursive Auto-encoder (BRAE) model and also with the help of linked Wordnet datasets.
机译：识别意见和给定数据集的分类以确定作者对给定数据的态度是肯定，否定还是中立的过程称为情感分析。情感分析涉及对给定数据集的观点的计算识别。它也被称为观点的提取。跨语言情感分析是指用两种语言生成意见。一种语言的资源高度丰富，提供了称为“资源丰富的语言”的意见提取所需的足够数据集。其他语言，例如卡纳达语，北印度语，马拉蒂语，它们的资源贫乏且缺乏数据Wordnet，并寻求资源丰富的语言的帮助以进行称为“资源贫乏的语言”的意见提取。情绪分析可能是电影评论和社交媒体回应的观点。在这里，我们使用了自动编码器的体系结构，该体系结构有助于生成两种语言的情感分析。可以通过使用双语约束递归自动编码器（BRAE）模型以及链接的Wordnet数据集来执行两种语言的情感分析。

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