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.
Highlights
Sentiment analysis (SA) is the use of natural language processing, statistics, or machine learning approaches to extract, identify, or otherwise describe the sentiment content of a text unit
This study analyzes how we can write an algorithm for performing Sentiment Analysis (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 if appropriate natural language processing algorithms are applied
Summary
Sentiment analysis (SA) is the use of natural language processing, statistics, or machine learning approaches to extract, identify, or otherwise describe the sentiment content of a text unit. SA or opinion mining is the computational study of people’s politics, attitudes, and emotions to elements such as topics, products, individuals, organizations, services, etc. In marketing it helps in determining the success of an ad campaign or new product launch, determine the versions of a product or service are accessible and even identify which demographics like or dislike particular features (Chenlo and Losada, 2014). Sentiment analysis is applied to product reviews but can be applied to stock markets (Hagenau et al, 2013), news articles (Xu et al, 2012) or political debates (Maks and Vossen, 2012). For example, we could analyze trends, identify ideological bias, target advertising/messages, gauging reactions, etc.
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