![]() ![]() The references listed below stand to establish its efforts in fulfilling the need of the day i.e. It has made use of all the opportunities given to it for making it suitable for digitalization and computerization. Malayalam has initiated its technological development well in advance. The work includes creation of a large size annotated corpus as a primary task and then followed by training a sentence level classifier to perform sentiment analysis. The learning carried out at two levels and the system classify sentences into positive, negative and neutral classes. This work presents a machine learning approach to sentiment analysis in Malayalam language using the CRF and SVM. Language and it does not possess a standard corpus or a sentiment lexicon. Sentiment analysis in Malayalam language has a large importance. The user generated text collected from social media can help machines to summarize and take intelligentÄecisions in different domains. Sentiment Analysis in social media, acquiring large importance today because people use social media platforms to share their views and opinions on relevant topics in the form of movie reviews, product reviews, political discussions etc. Sentiment analysis or opinion mining is a Natural Language Processing to find the emotions of public opinion from user generated text. ![]() The most similar sense cluster to the input text context is considered as the sense of the target word. ![]() These extended sets act as sense clusters. Seed set expansion module extends the seed set by adding most similar words to the seed set elements. Collocations and most co-occurring words are considered as training examples. For each possible sense of the ambiguous word, a relatively small set of training examples (seed sets) are identified which represents the sense. The proposed system uses a corpus which is collected from various Malayalam web documents. The aim of this work is to develop a WSD system for Malayalam, a language spoken in India, predominantly used in the state of Kerala. But Indian languages are morphologically rich and thus the processing task is very complex. Automatic WSD systems are available for structured languages like English, Chinese, etc. Since the sense of a word depends on its context of use, disambiguation process requires the understanding of word knowledge. The peculiarity of any language is that it includes a lot of ambiguous words. Word sense ambiguity arises when a particular word has more than one possible sense. WSD is very important as an intermediate step in many Natural Language Processing (NLP) tasks, especially in Information Extraction(IE), Machine Translation(MT) and Question/Answering Systems. Word Sense Disambiguation (WSD) is the task of identifying the correct sense of a word in a specific context when the word has multiple meaning. ![]()
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