Sentiment Analysis of Twitter Data: A Survey of Techniques

Abstract

With the advancement of web technology and its growth, there is a huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideas and sharing opinions. Social networking sites like Twitter, Facebook, Google+ are rapidly gaining popularity as they allow people to share and express their views about topics, have discussion with different communities, or post messages across the world. There has been lot of work in the field of sentiment analysis of twitter data. This survey focuses mainly on sentiment analysis of twitter data which is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous and are either positive or negative, or neutral in some cases. In this paper, we provide a survey and a comparative analyses of existing techniques for opinion mining like machine learning and lexicon-based approaches, together with evaluation metrics. Using various machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector Machine, we provide research on twitter data streams.We have also discussed general challenges and applications of Sentiment Analysis on Twitter.

Innovative Approaches in Sports Science—Lexicon-Based Sentiment Analysis as a Tool to Analyze Sports-Related Twitter Communication

“…Tweets contain a lot of information that cannot be usefully processed by lexicon-based sentiment analysis tools. Therefore, the following steps to clean the data-being in line with common approaches in the literature [27,28]-were executed automatically prior to the algorithmic evaluation: -Removing all URLs ("https://t.co/hSizQPxVFy") and mentions ("@CardiffCityFC") -Removing emoticons not analyzable as provided by the Twitter API in R ("<U+009F>") -…”

“…The algorithmic evaluation of tweets can be seen as a classification problem in which the tweets are assigned to two (positive vs. negative) or more categories. In the literature, the methods used are commonly divided into two groups: lexicon-based approaches and machine learning approaches [11,28,29]. Lexicon-based methods rely on a predefined list of words (lexicon) defining the semantic orientation of each word, such as positive and negative words or words with a specific positivity and negativity score.…”

“…In its simplest form, lexicon-based approaches [28,29] are based on the idea of having an extensive list of words that are either considered to have a positive or a negative meaning. Given the textual data of a tweet, the total number of words as well as the number of positive and negative words can be counted.…”

“…A straightforward and common measure in sentiment analysis is the accuracy of sentiment classification with regard to a dataset of positive and negative tweets, which is-roughly speaking-the proportion of correct classifications. It is defined as "Accuracy = (TP + TN)/(TP + TN + FP + FN)" [28] (p. 11) where TP (true positives) and TN (true negatives) refer to the number of tweets correctly classified (i.e., in line with the manual evaluation) as positive or negative by the algorithmic evaluation. FP (false positives) and FN (false negatives) refer to the number of tweets classified as positive or negative in contradiction to the manual evaluation.…”

“…This approach of creating a validation dataset [26] and assessing the accuracy [28,29,36] is common in the literature and the natural choice when sentiment analysis techniques are the main subject of investigation as its accuracy can be expressed in a single straightforward value. However, it cannot be considered sufficient with regard to testing the applicability of sentiment analysis in the sports domain for two reasons: First, the exclusion of neutral and nonsense tweets creates an artificial validation dataset that does not correspond exactly to real-world datasets.…”