FIELD

An exemplary embodiment relates to the field of sentiment analysis.

BACKGROUND

Sentiment analysis plays a major role in various text mining problems. However, there is a scarcity of research in the field of sentiment analysis for use in Arabic text. The Arabic language has multiple dialects used differently, for example, on social media. Thus, sentiment analysis of Arabic words creates a unique issue to be solved.

Subjective analysis provides insight into the subjective information found in the source material. For example, sentiment analysis might identify feelings associated with social media posts. Businesses can use sentiment analysis to identify and understand the social sentiment of their brand, product, or service by monitoring online conversations. A bag-of-words model used in natural language processing and information retrieval can represent text as a ‘bag’ or multiset of its words, sometimes disregarding grammar or word order.

Recently, Sentiment Analysis has played a major role in achieving different goals in many organizations. For example, businesses can use customer reviews to develop or improve their goods or services. Studies often rely on classifying text data based on two dimensions; either “yes or no”, or three dimensions, such as positive, negative, or neutral. The data is typically sourced from social media applications such as Twitter, Facebook, YouTube, and the like.

A variety of factors and issues, such as the form of the data, the language of text data, etc., may determine the usefulness of sentiment analysis results. Therefore, the processing of Arabic text data may need present a more difficult challenge than processing English text data because of the language rules, the data availability in the Arabic language and the paucity of studies conducted and applied to the Arabic language sentiment analysis.

Different techniques that have been applied to processing Arabic text data include using the lexicon approach, Part of Speech (POS) approach, word2vec and others. However, there was a contrast between the results, which indicated the complexity of processing the Arabic text data. The contrast resulted from different reasons such as the private letter in Arabic and the language dialects. The complexity of working with the Arabic language arises because of the number of dialects, which are more than five main groups from more than 15 countries. In addition, the Arabic speaking people often use common phrases that express direct or opposite meaning. Also, there are some words whose meaning varies according to the context.

Text Sentiment Analysis focuses on exploring the meaning behind the text. Therefore, the accuracy of the results is related to the language and its meanings. In “Analyzing sentiments expressed on twitter by UK energy company consumers” (Ikoro 2018) the authors analyzed the opinions of consumers of an electricity company about its services. They applied a combination of two lexicons in a Twitter text dataset, as compared to the traditional way of having only one lexicon. Since they used the unsupervised learning algorithms to determine the results, there was not any training data or testing data. The results showed that the negative and neutral tweets about the services of the new energy companies are more than the positive opinions about the old ones.

SUMMARY

According to at least one exemplary embodiment, a method for implementing sentiment analysis on text may be shown and described. An exemplary embodiment may implement a bag-of-phrases model. The method may begin by receiving and storing training data. The training data may be text data. The training data may be compiled and preprocessed by removing non-Arabic characters, numbers, control characters or graphics. Since Arabic words may include the same letters but written in a different format, an exemplary embodiment may identify common letters and unify them in order to remove or avoid duplicates. Further, the unifying of letters may also identify the meaning of each of the letters identified to be the same and may ensure that none of the unified letters are of a different format but also have a different meaning.

An annotator may label portions of the data, such as words, terms, or phrases, as positive, negative. An exemplary embodiment may implement additional labels, such as clear positive, clear negative, or neutral. A lexicon may be formed based on the labeled training data. The target text data may then be analyzed, and a bag-of-phrases may be formed from the analyzed target text data. Based on the distribution of words or phrases, a sentiment may be formed indicating a sentiment of each portion of the target data

