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%.

/pdf/predicting-stock-market-behavior-using-data-mining-technique-11iytmvtb7.pdf

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

Emotion detection and Sentiment analysis techniques are used to understand polarity or emotions expressed by people in many cases, especially during interactive systems use. Recognizing users’ emotions is an important topic for human–computer interaction. Computers that recognize emotions would provide more natural interactions. Also, emotion detection helps design human-centred systems that provide adaptable behaviour change interventions based on users’ emotions. The growing capability of machine learning to analyze big data and extract emotions therein has led to a surge in research in this domain. With this increased attention, it becomes essential to investigate this research area and provide a comprehensive review of the current state. In this paper, we conduct a systematic review of 123 papers on machine learning-based emotion detection to investigate research trends along many themes, including machine learning approaches, application domain, data, evaluation, and outcome. The results demonstrate: 1) increasing interest in this domain, 2) supervised machine learning (namely, SVM and Naïve Bayes) are the most popular algorithms, 3) Text datasets in the English language are the most common data source, and 4) most research use Accuracy to evaluate performance. Based on the findings, we suggest future directions and recommendations for developing human-centred systems. 

Machine learning techniques for emotion detection and sentiment analysis: current state, challenges, and future directions

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

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

Electronic waste or e-waste is a rapidly growing serious threat to the environment. The whole process of managing the e-waste which includes collection, transportation, storage, and recycling or treatment is yet to be well controlled. E-waste disposal has become an emerging issue for public health and the global environment. The convoluted chemicals produced during the e-waste recovery and hazardous by- products raise panic situations for the industry workers and labourers. Generally, the e-waste industry workers are untrained and belong to the informal sector. These people are generally not aware of the necessary precautionary actions to be taken before handling or recovering the e-waste. The inappropriate components, which can’t be reused, are either dumped or burnt outside openly. The toxic gases or hazardous chemicals pollute the environment or can be released directly into water sources. Thus, proper recycling of e-waste in a controlled way is very necessary and ecologically desirable. By promoting the reuse of non-degradable products their extraction rate can be minimized. A formalization of rules for e- waste management and their amendments can potentially offer benefits to this industry and the workers by reducing uncontrolled extraction and providing them employment security. This paper highlights the burden of e-waste, the impact of e-waste on health, the current status of e-waste management concerning the Moradabad region, initiatives for e-waste management including legislation, and current research aspects of e-waste management.

E-waste, a Global Challenge: Its Management and Impact on Public Health in India

Plant diseases are a severe cause of crop losses in the agriculture globally. Detection of diseases in plants is difficult and challenging due to the lack of expert knowledge. Deep learning-based models provide promising ways to identify plant diseases using leaf images. However, need of larger training sets, computational complexity, and overfitting, etc. are the major issues with these techniques that still need to be addressed. In this work, a convolutional neural network (CNN) is developed that consists of smaller number of layers leading to lower computational burden. Some augmentation techniques such as shift, shear, scaling, zoom, and flipping are applied to generate additional samples increasing the training set without actually capturing more images. The CNN model is trained for apple crop using a publicly available dataset PlantVillage to identify Scab, Black rot, and Cedar rust diseases in apple leaves. The rigorous experimental results revealed that the proposed model is well fit to identify apple leaf diseases and achieves 98% classification accuracy. It is also evident from the results that it needs lesser amount of storage and takes smaller execution time than several existing deep CNN models. Although, there exist several CNN models for crop disease detection with comparable accuracy, but the proposed model needs lower storage and computational resources. Therefore, it is highly suitable for deploying in handheld devices.

Detection of Apple Plant Diseases Using Leaf Images Through Convolutional Neural Network

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.

/pdf/sentiment-analysis-on-movie-reviews-a-comparative-study-of-55ym8y4kxh.pdf

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

Sentiment analysis technique plays an important role in natural language processing to analyze complex human statements. In the last few years, this technique has become a powerful tool for several social media communication mediums such as WhatsApp, Twitter, Facebook, Instagram, YouTube, LinkedIn, Blog, etc. This paper proposes a machine learning (ML) based method to analyze social media data for sentiment analysis on text data. The presented method is divided into three distinct stages. In the first stage, pre-processing is performed to filter and refine the text data. In the second stage, the feature extraction is performed using the Term Frequency and Inverse Document Frequency (TF-IDF) technique. Moreover, during the third stage, the extracted features are supplied to make predictions for the classifier. The experiments are carried out on a publicly available Twitter dataset for US Airlines. Several ML techniques are utilized for analysis and classification. The results are reported for different evaluation metrics like accuracy, precision, recall, and F1 score. Finally, the support vector machine yielded the most relevant results.

Sentiment Analysis of Twitter Data Using TF-IDF and Machine Learning Techniques

Paddy yield prediction based on 2D images of rice panicles using regression techniques

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