Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews

This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., "subtle nuances") and a negative semantic orientation when it has bad associations (e.g., "very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word "excellent" minus the mutual information between the given phrase and the word "poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews

Analyzing User Behavior of Social Media

In recent time, analysis the user behavior from social media sites and applications plays a important role in current scenario. The users are people who are using social media sites and applications. To analyzing the behavior researcher deals with collecting data by opinions, identifying them. Classifying data according to the orientation of opinions and presenting their behavior from data. Analysis of behavior is an interesting job but it is also a very challenging procedure. Most of social media provides huge opinions on a particular product. The response/ opinions by user play a significant role in deciding the product�s future. These responses create positive or negative impact which plays a vital role in the field of e-commerce. It provides the useful result about product which have demands in the market or not, which is very useful for both customers as well as supplier of that product. In this paper researcher purposed some steps to analyze and express their views how social media data analyze market behavior for both company and customer

Sentiment Analysis of Twitter Data Using Naive Bayes Algorithm

Sentiment analysis the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer’s attitude towards a particular topic, product, etc. is positive, negative, or neutral. Now a days the growth of social websites, blogging services and electronic media con-tributes huge amount of user give messages such as customer reviews, comments and opinions. Sentiment Analysis is an important term referred to collect information in a source by using NLP, computational linguistics and text analysis and to make decision by subjective information extracting and analyzing opinion, identifying positive and negative reviews measuring how positively and negatively an entity (public ,organization, product) is involved. Sentiment analysis is the area of study to analyze people’s reviews, emotion, attitudes and emotion from written languages. We concentrate on field of different opinion classification techniques, performed on any data set. Now a days most popular approaches are Bag of words and feature extraction used by researchers to deal with sentiment analysis i.e used by politician, news groups, manufactures organization, movies, products etc

Considering Two Sides of One Review Using Stanford NLP Framework

Sentiment analysis is a type of natural language processing for tracking the mood of the public about a particular product or a topic and is useful in several ways. Polarity shift is the most classical task which aims at classifying the reviews either positive or negative. But in many cases, in addition to the positive and negative reviews, there still many neutral reviews exist. However, the performance sometimes limited due to the fundamental deficiencies in handling the polarity shift problem. We propose an Improvised Dual Sentiment Analysis (IDSA) model to address this problem for sentiment classification. We first propose a novel data expansion technique by creating sentiment-reversed review for each training and test review. We develop a corpus based method to construct a pseudo-antonym dictionary. It removes DSA’s dependency on an external antonym dictionary for review reversion. We conduct a range of experiments and the results demonstrates the effectiveness of DSA in addressing the polarity shift in sentiment classification.

Automatic Dream Sentiment Analysis

In this position paper, we propose a first step toward automatic analysis of sentiments in dreams. 100 dreams were sampled from a dream bank created for a normative study of dreams. Two human judges assigned a score to describe dream sentiments. We ran four baseline algorithms in an attempt to automate the rating of sentiments in dreams. Particularly, we compared the General Inquirer (GI) tool, the Linguistic Inquiry and Word Count (LIWC), a weighted version of the GI lexicon and of the HM lexicon and a standard bag-of-words. We show that machine learning allows automating the human judgment with accuracy superior to majority class choice

Opinion Mining on Non-English Short Text

As the type and the number of such venues increase, automated analysis of sentiment on textual resources has become an essential data mining task. In this paper, we investigate the problem of mining opinions on the collection of informal short texts. Both positive and negative sentiment strength of texts are detected. We focus on a non-English language that has few resources for text mining. This approach would help enhance the sentiment analysis in languages where a list of opinionated words does not exist. We propose a new method projects the text into dense and low dimensional feature vectors according to the sentiment strength of the words. We detect the mixture of positive and negative sentiments on a multi-variant scale. Empirical evaluation of the proposed framework on Turkish tweets shows that our approach gets good results for opinion mining

A cloud-based tool for sentiment analysis in reviews about restaurants on TripAdvisor

The tourism industry has been promoting its products and services based on the reviews that people often write on travel websites like TripAdvisor.com, Booking.com and other platforms like these. These reviews have a profound effect on the decision making process when evaluating which places to visit, such as which restaurants to book, etc. In this contribution is presented a cloud based software tool for the massive analysis of this social media data (TripAdvisor.com). The main characteristics of the tool developed are: i) the ability to aggregate data obtained from social media; ii) the possibility of carrying out combined analyses of both people and comments; iii) the ability to detect the sense (positive, negative or neutral) in which the comments rotate, quantifying the degree to which they are positive or negative, as well as predicting behaviour patterns from this information; and iv) the ease of doing everything in the same application (data downloading, pre-processing, analysis and visualisation). As a test and validation case, more than 33.500 revisions written in English on restaurants in the Province of Granada (Spain) were analyse