Abstract

Web optimization is the process of optimizing the web to increase visibility or rank of websites in search engines. Furthermore, this process is also viewed from multiple perspectives, from optimizing inter-server communication that offers the best responses to users’ queries and provides targeted advertisements to users of a website. With this regard, the process of automatic classification and information extraction from users’ comments, also known as Sentiment Analysis (SA) or opinion mining, becomes vital to offer users the best online experience, based on their preferences. There are numerous algorithms available for SA. Therefore before applying any algorithm for polarity detection, pre-processing on comments is carried out. This study analyzes how we can write an algorithm for performing SA, and how different types of processing that are applied to initial data such as stemming or eliminating stop words affect the performance of this algorithm. The results show that even when a small sample is used, sentiment analysis can be done with a high accuracy (over 70%) if appropriate natural language processing algorithms are applied. Having a method for guessing sentiments could enable us, to excerpt opinions from the internet and predict online customer’s favorites, which could ascertain valuable for commercial or marketing research.

Key words: Sentiment analysis, natural language processing, python programming language, machine learning, web optimization.