Mining of Malay Newspaper ( SAMNews ) Using Artificial Immune System

IAES International Journal of Artificial Intelligence (IJ-AI)

The popularity of the social media channels has increased the interest among researchers in the sentiment analysis(SA) area. One aspect of the SA research is the determination of the polarity of the comments in the social media, i.e. positive, negative, and neutral. However, there is a scarcity of Malay sentiment analysis tools because most of the work in the literature discuss the polarity classification tool in English. This paper presents the development of a polarity classification tool called Malay Polarity Classification Tool(MaCT). This tool is developed based on the AFINN sentiment lexicon for English language. We have attempted to translate each word in AFINN to its Malay equivalent and later, use the lexicon to collect the sentiment data from Twitter. The Twitter data are then classified into positive, negative, and neutral. For the validation purpose, we collect 400 positive tweets, 400 negative tweets, and 200 neutral tweets, and later, run the tweets through our sentime...

. In the advancement of technology, the web era revolutionized mankind's life; huge amounts of data are available on the internet in the form of articles and blogs. From this huge volume of data, opinion mining is important for extracting the raw data to become useful information. Sentiment analysis provides categorization in opinion mining as a positive or negative class for content analysis. English language is considered as a universal language and used almost every part of the word, so classification of opinion is important to get the end meaning of the word phrase and comments. No literature is available for classification of sub opinion in the text mining. SAP of Text through Machine Learning algorithm (KNN) is a three-step technique of opinion mining. In this study, authors have put articles at first removing stop-words, tokenizing the sentence and revamping the tokens, it will calculate the polarity of the sentence, paragraph, and text through contributing weighted words by keeping sentiment shifters and intensity clauses in consideration. Secondly, the over the polarization of the sentence is adjusted. Finally, the overall trend of the input text on the basis of tokenization and polarization of the sentence is predicted with the proposed algorithm and compared with KNN. Furthermore, domain-specific analysis is a distinct feature of the proposed model where data can be updated according to the required domain to ensure the optimal level of efficiency.

PloS one, 2018

Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. In this paper, a Malay sentiment analysis classification model is proposed to improve classification performances based on the semantic orientation and machine learning approaches. First, a total of 2,478 Malay sentiment-lexicon phrases and words are assigned with a synonym and stored with the help of more than one Malay native speaker, and the polarity is manually allotted with a score. In addition, the supervised machine learning approaches and lexicon knowledge method are combined for Malay sentiment classification with evaluating thirteen features. Finally, three individual classifiers and a combined classifier are used to evaluate the classification accuracy. In experimental results, a wide-range of comparative experiments is conducted on a Malay Reviews Corpus (MRC), and it...

The positive-negative classification criterion is one of the most important to classify a text. The first studies date back to Osgood, Suci and Tannenbaum (1957), with reference to the main dimensions of meaning identified in evaluation, potency and activity. Later on Boucher e Osgood formulated the well known Pollyanna hypothesis (1969). Recently Bolasco and della Ratta-Rinaldi (2004) have taken up their work on this hypothesis with the aim to build an analysis tool capable of categorizing a text moving from the presence of the evaluative elements it contains. In our paper, we wish to develop this topic from the point of view of the validity and reliability of the positive-negative criterion when we make use of techniques of automatic text classification. From the reliability point of view we have to check the method of selection of the negativity index, according to which in an automatic analysis it is not possible to ignore the overall context of measurement. These methodological considerations have been applied to a corpus in English of articles about China taken from four UK newspapers: The Times, The Guardian, The Independent and The Daily Telegraph from 2000 to 2008. Keywords: text mining, categorisation, evaluation, positive and negative

Advances in Environmental Biology

With the genesis of the internet and the world wide web, we have seen an unprecedented growth of data and information on the web as well as a huge growth in digital or textual opinions, sentiments and attitudes as remarked upon in reviews. Special attention needs to be given to the processing and understating information used by information retrieval methods and natural language processing methods. One of the main problems in this scope is sentiment analysis whereby a review is classified into two classes, i.e. positive (thumbs up or favourable) or negative (thumbs down or unfavourable) opinions. This paper discusses related issues such as feature selection methods and different sentiment classifications as well as the main approaches currently being taken to solve this problem.

ijcsi.org

This literature work is a survey about Sentiment Analysis of textual data of English language and some of its previous works. Basically sentiment analysis identifies the view point or opinion of a text. For example, classifying a movie review as "Thumbs up" or "Thumbs down". Several public opinion surveys from multiple polling organization and people's aggregate opinion on a topic can be assessed. This survey includes previous works which show how this technique has evolved over the past one and a half decade expanding its horizon and reaching out to almost all areas such as reviews of products, movies etc., travel advice, stock market predictions and in other decision making areas.

In daily life customer reviews or opinions play a very key role. Whenever anyone has to take a decision, they are likely to consider opinions of others. Analyzing the people " s feeling is significant for many applications such as companies, ventures, enterprises trying to find out the review of their goods in the marketplace, predicting socioeconomic situations like stock exchange and predicting political elections. Since there is a good quantity of collective data present on the internet automatically therefore, it " s very important to employ techniques that automatically classify them. Sentiment Classification is also called as Opinion Mining. This project addresses sentiment analysis in Hotel reviews that classifies the hotel reviews according to the sentiment expressed by the public into: positive or negative. To classify the hotel reviews i.e. the given sentiment we used natural language tool kit (NLTK) to pre-process the natural language tasks. While pre-processing the text review first we remove stop words, punctuations and regular expressions .We also used n-grams to improve the sentimental analysis. After pre-processing the sentiment we choose a classifier to classify the sentiment. So, in order to selecting the best classifier in this paper we use different classifiers of different models to classify the sentiment and we also give the brief comparison of classifiers in terms of Accuracy, Precision, Recall and F-Measure. With these evaluation metrics we can show the best classifier. Multinomial NaiveBayes is the most accurate classifier to our data set and it gives quite efficient results than other classifiers.

2014

The field of information extraction and retrieval has grown exponentially in the last decade. Sentiment analysis is a task in which you identify the polarity of given text using text processing and classification. There are various approaches in the task of classification of text into various classes. Use of particular algorithms depends on the kind of input provided. Analyzing and understanding when to use which algorithm is an important aspect and can help in improving accuracy of results. Keywords— Sentiment Analysis, Classification Algorithms, Naïve Bayes, Max Entropy, Boosted Trees, Random Forest.

Journal of Software Engineering & Intelligent Systems, 2019

As the use of the internet is continuously increasing, there are a huge amount of opinions available online. This is proven by the existence of a site specifically made for book reviews called Goodreads (www.goodreads.com). In this site, the user can freely express their opinions, give an assessment through star-rating, and write a review for specific book they read. The abundance of information from unstructured data like this encourages the emergence of knowledge in text analysis or also known as sentiment analysis. The complexity of analysis sentiment includes selecting appropriate classification algorithm. Other than that, a problem often found in classifying text is imbalanced dataset that can cause seriously negative effect on classifier's performance of machine learning algorithm. The purpose of this research is to compare 3 classifier's performance, which are Naïve Bayes, Random Forest and K-Nearest Neighbor, in dealing with imbalanced dataset. The classification is done for 3 different comparison ratios, which are 90%:10%, 80%:20%, and 70%:30%, and 3 different random sampling values or known as random states, which are 0, 10, and 20. The performance is assessed based on accuracy, confusion matrix, and classification report that includes precision, recall, and F1-score calculation.

International Journal of Sensors, Wireless Communications and Control, 2020

Aims: The proposed research work is on an evolutionary enhanced method for sentiment or emotion classification on unstructured review text in the big data field. The sentiment analysis plays a vital role for current generation of people for extracting valid decision points about any aspect such as movie ratings, education institute or politics ratings, etc. The proposed hybrid approach combined the optimal feature selection using Particle Swarm Optimization (PSO) and sentiment classification through Support Vector Machine (SVM). The current approach performance is evaluated with statistical measures, such as precision, recall, sensitivity, specificity, and was compared with the existing approaches. The earlier authors have achieved an accuracy of sentiment classifier in the English text up to 94% as of now. In the proposed scheme, an average accuracy of sentiment classifier on distinguishing datasets outperformed as 99% by tuning various parameters of SVM, such as constant c value a...