Thesis Opinion Mining

Thesis Opinion Mining-75
In the Classification phase, the decision to annotate the tweets as negative, positive, or neutral towards a specific topic was made using a trained machine-learning algorithm.

In the Classification phase, the decision to annotate the tweets as negative, positive, or neutral towards a specific topic was made using a trained machine-learning algorithm.Results from different feature sets, classifiers, and datasets are reported in terms of classification accuracy, Kappa statistic, and F-measure.Thanks to Essay USA you can buy an essay now and get an essay in 14 days or 8 hours – the quality will be equally high in both cases.

A Master of Science thesis in Computer Engineering by Soha Galalaldin Khider Ahmed entitled, "Sentiment Mining of Arabic Twitter Data," submitted in January 2014. One key factor to their attractiveness worldwide is that these sites and services allow people to express and share their opinions, likes, and dislikes, freely and openly.

Social networking services such as Facebook and Twitter and social media hosting websites such as Flickr and You Tube have become increasingly popular in recent years.

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Opinion Driven Decision Support System (ODSS) refers to the use of large amounts of online opinions to facilitate business and consumer decision making.

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In e-commerce, online shopping and online tourism, its very crucial to analyse the good amount of social data present on the Web automatically therefore, its very important to create methods that automatically classify them.

Opinion Mining sometimes called as Sentiment Classification is defined as mining and analysing of reviews, views, emotions and opinions automatically from text, big data and speech by means of various methods.

In this thesis we are going to see how Apriori frequent item set mining algorithm can be used for mining reviews from online reviews those are posted by customers.

Our main theme is to create a system for analysing opinions which implies judgement of different consumer products.

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