Stock market prediction has become an attractive investigation topic due to its important role in economy and beneficial offers. There is an imminent need to uncover the stock market future behavior in order to avoid investment risks. The large amount of data generated by the stock market is considered a treasure of knowledge for investors. This study aims at constructing an effective model to predict stock market future trends with small error ratio and improve the accuracy of prediction. This prediction model is based on sentiment analysis of financial news and historical stock market prices. This model provides better accuracy results than all previous studies by considering multiple types of news related to market and company with historical stock prices. A dataset containing stock prices from three companies is used. The first step is to analyze news sentiment to get the text polarity using naïve Bayes algorithm. This step achieved prediction accuracy results ranging from 72.73% to 86.21%. The second step combines news polarities and historical stock prices together to predict future stock prices. This improved the prediction accuracy up to 89.80%.

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Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis

Does government information release really matter in regulating contagion-evolution of negative emotion during public emergencies? From the perspective of cognitive big data analytics

Consumers evaluate products through online reviews, in addition to sharing their product experiences. Online reviews affect product marketing, and companies use online reviews to investigate consumer attitudes and perceptions of their products. However, when analyzing a review, it is often the case that specific contexts are not taken into consideration and meaningful information is not obtained from the analysis results. This study suggests a methodology for analyzing reviews in the context of comparing two competing products. In addition, by analyzing the discriminative attributes of competing products, we were able to derive more specific information than an overall product analysis. Analyzing the discriminative attributes in the context of comparing competing products provides clarity on analyzing the strengths and weaknesses of competitive products and provides realistic information that can help the company's management activities. Considering this purpose, this study collected a review of the BB Cream product line in the cosmetics field. The analysis was sequentially carried out in three stages. First, we extracted words that represent discriminative attributes by analyzing the percentage difference of words. Second, different attribute words were classified according to the meaning used in the review by using latent semantic analysis. Finally, the polarity of discriminative attribute words was analyzed using Labeled-LDA. This analysis method can be used as a market research method as it can extract more information than a traditional survey or interview method, and can save cost and time through the automation of the program.

Analyzing the discriminative attributes of products using text mining focused on cosmetic reviews

For the current online reviews sentiment classification method, there are some problems such as serious text sparseness and coarse granularity of sentiment calculation. In this paper, the emotion in online reviews is divided into four categories: happiness, hope, disgust, and anxiety. Based on the combination of cognitive evaluation theory and sentiment analysis, a novel approach that combines a well-known techniques to sentiment classification, ie, support vector machine and the latent semantic analysis, was proposed. Based on the approach, this paper explored the influence of these four kinds of emotions on the helpfulness of online reviews, examined the moderating effects of emotion on the helpfulness of online reviews under the two types of products. The experimental results showed that this model could effectively conduct multi-emotion fine-grained computing for online reviews, improve the accuracy and computational efficiency of sentiment classification. The final empirical analysis found that happiness and disgust emotion had significant positive impact on the helpfulness of online reviews, while on the other hand anxiety emotion had significant negative influence. The algorithm and its empirical conclusions provide useful theoretical basis and reference for the company to optimize marketing strategy and improve customer relationship under web 2.0.

Sentiment classification and computing for online reviews by a hybrid SVM and LSA based approach

In this paper Authors explained their experimental works. In this work we choose movies review from corpora community. corpora contain numbers of dataset from where we selected movies review. In this dataset we have two categories negative & positive each category contains 1000 files. In our file we have movies review by different reviewer. we took. txt file extension. Here we are finding sentiment polarity against those movies. we extend some more in comparison to our base papers. we apply numbers of classification algorithm like NaiveBayes, SkLearn, Support Vector Machine. We applied multiple classifier like MultinomialNB, GaussianNB, BernoulliNB and many more. we select different features (in count or numbers) to understand Accuracy Level of different Algorithms. Here Authors used Ensemble Learning where we combined number of classification algorithms and try voted algorithm to find best accuracy.

Sentiment Classification of Movie Reviews by Supervised Machine Learning Approaches Using Ensemble Learning & Voted Algorithm

Social Networks such as Facebook, Twitter, Linked In etc... are rich in opinion data and thus Sentiment Analysis has gained a great attention due to the abundance of this ever growing opinion data. In this research paper our target set is movie reviews. There are diverge range of mechanisms to express their data which may be either subjective, objective or a mixture of both. Besides the data collected from World Wide Web consists of lot of noisy data. It is very much true that we are going to apply some pre-processing techniques and compare the accuracy using Machine Learning algorithm Naïve Bayes Classifier. With ever growing demand to mine the Big Data the open source software technologies such as Hadoop using map reducing paradigm has gained a lot of pragmatic importance. This paper illustrates a comparitive study of sentiment analysis of movie reviews using Naïve Bayes Classifier and Apache Hadoop in order to calculate the performance of the algorithms and show that Map Reduce paradigm of Apache Hadoop performed better than Naïve Bayes Classifier.

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B. Narendra

Papers

Sentiment Analysis on Movie Reviews: A Comparative Study of Machine Learning Algorithms and Open Source Technologies