A large number of sentiment reviews, blogs and comments present online.These reviews must be classified to obtain a meaningful information.Four different supervised machine learning algorithm used for classification.Unigram, Bigram, Trigram models and their combinations used for classification.The classification is done on IMDb movie review dataset. With the ever increasing social networking and online marketing sites, the reviews and blogs obtained from those, act as an important source for further analysis and improved decision making. These reviews are mostly unstructured by nature and thus, need processing like classification or clustering to provide a meaningful information for future uses. These reviews and blogs may be classified into different polarity groups such as positive, negative, and neutral in order to extract information from the input dataset. Supervised machine learning methods help to classify these reviews. In this paper, four different machine learning algorithms such as Naive Bayes (NB), Maximum Entropy (ME), Stochastic Gradient Descent (SGD), and Support Vector Machine (SVM) have been considered for classification of human sentiments. The accuracy of different methods are critically examined in order to access their performance on the basis of parameters such as precision, recall, f-measure, and accuracy.

Classification of sentiment reviews using n-gram machine learning approach

. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Furthermore, decision makers need to be able to gain valuable insights from such varied and rapidly changing data, ranging from daily transactions to customer interactions and social network da-ta. Such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper aims to analyze some of the different analytics methods and tools which can be applied to big data, as well as the opportunities provided by the application of big data analytics in various decision domains. Keywords: big data, data mining, analytics, decision making.

Big Data Analytics: A Literature Review Paper

Can the choice of words and tone used by the authors of financial news articles correlate to measurable stock price movements? If so, can the magnitude of price movement be predicted using these same variables? We investigate these questions using the Arizona Financial Text (AZFinText) system, a financial news article prediction system, and pair it with a sentiment analysis tool. Through our analysis, we found that subjective news articles were easier to predict in price direction (59.0% versus 50.0% of chance alone) and using a simple trading engine, subjective articles garnered a 3.30% return. Looking further into the role of author tone in financial news articles, we found that articles with a negative sentiment were easiest to predict in price direction (50.9% versus 50.0% of chance alone) and a 3.04% trading return. Investigating negative sentiment further, we found that our system was able to predict price decreases in articles of a positive sentiment 53.5% of the time, and price increases in articles of a negative sentiment 52.4% of the time. We believe that perhaps this result can be attributable to market traders behaving in a contrarian manner, e.g., see good news, sell; see bad news, buy.

https://www.robschumaker.com/publications/DSS%20-%20Evaluating%20Sentiment%20in%20Financial%20News%20Articles.pdf

Evaluating sentiment in financial news articles

Predicting the direction of stock market prices using tree-based classifiers

Predicting trends in stock market prices has been an area of interest for researchers for many years due to its complex and dynamic nature. Intrinsic volatility in stock market across the globe makes the task of prediction challenging. Forecasting and diffusion modeling, although effective can't be the panacea to the diverse range of problems encountered in prediction, short-term or otherwise. Market risk, strongly correlated with forecasting errors, needs to be minimized to ensure minimal risk in investment. The authors propose to minimize forecasting error by treating the forecasting problem as a classification problem, a popular suite of algorithms in Machine learning. In this paper, we propose a novel way to minimize the risk of investment in stock market by predicting the returns of a stock using a class of powerful machine learning algorithms known as ensemble learning. Some of the technical indicators such as Relative Strength Index (RSI), stochastic oscillator etc are used as inputs to train our model. The learning model used is an ensemble of multiple decision trees. The algorithm is shown to outperform existing algo- rithms found in the literature. Out of Bag (OOB) error estimates have been found to be encouraging. Key Words: Random Forest Classifier, stock price forecasting, Exponential smoothing, feature extraction, OOB error and convergence.

Predicting the direction of stock market prices using random forest

Mining is used to help people to extract valuable information from large amount of data. Sentiment analysis focuses on the analysis and understanding of the emotions from the text patterns. It identifies the opinion or attitude that a person has towards a topic or an object and it seeks to identify the viewpoint underlying a text span. Sentiment analysis is useful in social media monitoring to automatically characterize the overall feeling or mood of consumers as reflected in social media toward a specific brand or company and determine whether they are viewed positively or negatively on the web. This new form of analysis has been widely adopted in customer relation management especially in the context of complaint management. For automating the task of classifying a single topic textual review, document-level sentiment classification is used for expressing a positive or negative sentiment. So analyzing sentiment using Multi-theme document is very difficult and the accuracy in the classification is less. The document level classification approximately classifies the sentiment using Bag of words in Support Vector Machine (SVM) algorithm. In proposed work, a new algorithm called Sentiment Fuzzy Classification algorithm with parts of speech tags is used to improve the classification accuracy on the benchmark dataset of Movies reviews dataset.

Sentiment analysis and classification based on textual reviews

Nowadays stock market is the one of the major sources of raising resources for India and is act as a key driver for economic growth of a country. The stock market forecasting is a very difficult and highly complicated task because it is affected by many factors such as economic conditions, investor's sentiments and political events etc. The stock market series are generally dynamic, nonparametric, noisy and chaotic by nature. Although many soft computing techniques have been widely used, Support Vector Machine (SVM) has gained its popularity and it has outperformed Artificial Neural Network (ANN) also. Even though SVM is novel and performs best in many applications, the practicality of SVM is impacted due to the problems of choosing suitable parameters of SVM (C, σ and e). The Cuckoo Search (CS) is based on the Swarm Intelligence optimization technique and is very simple to tune the parameters of SVM. The proposed hybrid CS-SVM technique has been proven to be able to generate better results when compared to ANN and SVM in the prediction of the stock price movement.

Cuckoo optimized SVM for stock market prediction

The user generated content on the web grows rapidly in this emergent information age. The evolutionary changes in technology make use of such information to capture only the user's essence and finally the useful information are exposed to information seekers. In this paper we detect online hotspot forums by computing sentiment analysis for text data available in each forum. This approach analyzes the forum text data and computes sentiment score for each word or phrase of text. Then we propose two text mining approaches like K-means clustering and Support Vector Machine with Particle Swarm Optimization (PSO-SVM) classification algorithms that can be used to group into two forum clusters forming hotspot forums and non-hotspot forums within each time window. The text datasets that we use in our experimental research are collected from forums.digitalpoint.com and after data cleaning they are formatted to 37 different forums and 1616 threads. The experiment helps to identify that K-means and PSO-SVM together achieve highly consistent results.

A semantic enhanced approach for online hotspot forums detection

Problem statement: Online forums hotspot prediction is one of the sig nificant research areas in web mining, which can help people make proper decision in daily life. Online forums, news reports and blogs, are containing large volume of public op inion information. Rapid growth of network arouses much attention on public opinion, it is important t o analyse the public opinion in time and understand s the trends of their opinion correctly. Approach: The sentiment analysis and text mining are importa nt key elements for forecasting the hotspots in online forums. Most of the traditional text mining work o n static data sets, while the online hotspot forecast s works on the web information dynamically and timely. The earlier work on text information processing foc uses in the factual domain rather than opinion domain. Due to the semi structured or unstructured characteristics of online public opinion, we introd uce traditional Vector Space Model (VSM) to express them and then use K-means to perform hotspot detection, then we use J48 classifier to perform ho tspot forecast. Results: The experimentation is conducted by Rapid Miner tool and performance of proposed method J48 is compared with other method, such as Naive Bayes. The consistency between K-means and J48 is validated using three metrics. They are accuracy, sensitivity and specifi city. Conclusion: The experiment helps to identify that K-means and J48 together to predict forums hotspot. The results that have been obtained using J48 present a noticeable consistency with the results a chieved by K-means clustering.

/pdf/online-forums-hotspot-prediction-based-on-sentiment-analysis-m9y18fbv7d.pdf

K. Nirmala Devi

Papers

Sentiment analysis and classification based on textual reviews

Cuckoo optimized SVM for stock market prediction

A semantic enhanced approach for online hotspot forums detection

Online Forums Hotspot Prediction Based on Sentiment Analysis

An Efficient AlexNet Deep Learning Architecture for Automatic Diagnosis of Cardio-Vascular Diseases in Healthcare System