مقاله انگلیسی رایگان در مورد الگوریتم های یادگیری ماشین – الزویر 2019

1. Introduction

2. Related Work

3. The Proposed Methodology

4. Experiment Results and Discussion

5. Conclusions and Future Work

6. References

Sentiment analysis (SA) is a scholarly process of extricating and classifying individuals’ emotions and feedbacks expressed in source text content. It is one of the pursued subfields of Computational Linguistics (CL) and Natural Language Processing (NLP). The evolution of social media based applications has generated a big amount of personalized reviews of different related information on the Web in the form of tweets, status updates, and many others. Several approaches have come into the spotlight in recent years to accomplish SA, the most part of SA researches have been applied utilizing the English language. SA in Arabic online social media may be slacking behind commonly because of the difficulties with handling the morphologically complex Arabic natural language and the lack and absence of accessible tools and assets for extracting Arabic opinions from the text. This research is aimed to analyze the collected twitter posts in different Arabic Dialects and a comparison between the various algorithms used for SA with various n-gram as a feature extraction method. The measurement of the performance of different algorithms is evaluated in terms of recall, precision, f-measure, and accuracy. The experiment results show that unigram with Passive Aggressive (PA) or Ridge Regression (RR) gives the highest accuracy 99.96 %.

Introduction

Sentiment analysis, also called Opinion Mining (OM) is the field that investigates and analyzes individuals’ reactions and responses towards an entity (e.g. Blogs, movies, products, DVD, books…) utilizing text analysis algorithms to determine individual textual attitude1. SA acts like an effective and powerful tool for individuals to extricate the essential information, also to aggregate and mixture the collective sentiments of the reviews. Utilizing SA, variances in stock prices could be predicted2, political election race preferences can be observed closely3, and even groups’ interactions could be observed and followed which provides many advantages and benefits4. As individuals, there is always a tendency to consult close friends and relatives about items before purchasing them.