Traditional sentiment classification methods often require polarity dictionaries or crafted features to utilize machine learning. However, those approaches incur high costs in the making of dictionaries and/or features, which hinder generalization of tasks. Examples of these approaches include an approach that uses a polarity dictionary that cannot handle unknown or newly invented words and ano...

Due to the explosive growth of social media usage in Thailand, many businesses and organizations including market research agencies are seeking for tools which could perform real-time sentiment analysis on the large contents. In this paper, we propose S-Sense, a framework for analyzing sentiment on Thai social media. The proposed framework consists of analysis modules and language resources. Tw...

Dictionaries have been used to analyse text even before the emergence of social media and the use of dictionaries for sentiment analysis there. While dictionaries have been used to understand the tonality of text, so far it has not been possible to automatically detect if the tonality refers to the present, past, or future. In this research, we develop a dictionary containing time-indicating wo...

To study the sentiment diffusion of online public opinions about hot events, we collected people's posts through web data mining techniques. We calculated the sentiment value of each post based on a sentiment dictionary. Next, we divided those posts into five different orientations of sentiments: strongly positive (P), weakly positive (p), neutral (o), weakly negative (n), and strongly negative...

Hit and hot issue about reviews of any product is sentiment classification. Not only manufacturing company of the reviewed product takes decision about its quality, but the customers’ purchase of the product is also based on the reviews. Instead of reading all the reviews one by one, different works have been done to classify them as negative or positive with preprocessing. Suppose from 1000 re...

This report details the findings in building a naive Bayes sentiment classifier for a IMDB movie-review data set using Scala and ScalaNLP. We studied the unigram or bagof-words Bernoulli and Multinomial models and a number of different feature selection techniques, including term frequency, mutual information and Chi-squared. 1. DATA CORPUS The corpus contains of 2000 rated movie reviews, compr...

In this paper we describe the probabilistic model that we used in the CrowdScale – Shared Task Challenge 2013 for processing the CrowdFlower dataset, which consists of a collection of crowdsourced text sentiment judgments. Specifically, the dataset includes 569,786 sentiment judgments for 98,979 tweets, discussing the weather, collected from 1,960 judges. The challenge is to compute the most re...

The use of Machine Learning (ML) and Sentiment Analysis (SA) on data from microblogging sites has become a popular method for stock market prediction. In this work, we developed model predicting movement utilizing SA Twitter StockTwits data. Stock sentiment were used to evaluate approach validate it Microsoft stock. We gathered tweets StockTwits, as well financial Finance Yahoo. was applied twe...