Sentiment Analysis of Afaan Oromoo Facebook Media Using Deep Learning Approach

doi:10.7176/nmmc/90-02

Abstract

The rapid development and popularity of social media and social networks provide people with unprecedented opportunities to express and share their thoughts, views, opinions and feelings about almost anything through their personal webpages and blogs or using social network sites like Facebook, Twitter, and Blogger. This study focuses on sentiment analysis of social media content because automatically identifying and classifying opinions from social media posts can provide significant economic values and social benefits. The major problem with sentiment analysis of social media posts is that it is extremely vast, fragmented, unorganized and unstructured. Nevertheless, many organizations and individuals are highly interested to know what other peoples are thinking or feeling about their services and products. Therefore, sentiment analysis has increasingly become a major area of research interest in the ﬁeld of Natural Language Processing and Text Mining. In general, sentiment analysis is the process of automatically identifying and categorizing opinions in order to determine whether the writer's attitude towards a particular entity is positive or negative. To the best of the researcher’s knowledge, there is no Deep learning approach done for Afaan Oromoo Sentiment analysis to identify the opinion of the people on social media content. Therefore, in this study, we focused on investigating Convolutional Neural Network and Long Short Term Memory deep learning approaches for the development of sentiment analysis of Afaan Oromoo social media content such as Facebook posts comments. To this end, a total of 1452 comments collected from the official site of the Facebook page of Oromo Democratic Party/ODP for the study. After collecting the data, manual annotation is undertaken. Preprocessing, normalization, tokenization, stop word removal of the sentence are performed. We used the Keras deep learning python library to implement both deep learning algorithms. Long Short Term Memory and Convolutional Neural Network, we used word embedding as a feature. We conducted our experiment on the selected classifiers. For classifiers, we used 80% training and 20% testing rule. According to the experiment, the result shows that Convolutional Neural Network achieves the accuracy of 89%. The Long Short Memory achieves accuracy of 87.6%. Even though the result is promising there are still challenges. Keywords : Sentiment Analysis; Opinionated Afaan Oromoo facebook comments; Oromo Democratic Party Facebook page DOI: 10.7176/NMMC/90-02 Publication date: May 31 st 2020

Highlights

The revolution of web2.0 and the increasing numbers of blogs, social media networks, web reviews, and many others have fundamentally changed the way people express their opinions and share information on the Internet
Based on many kinds of literature we explored that Long Short-Term Memory (LSTM) is the more advantageous state of the art neural network algorithm for sentiment analysis
We proposed the Convolutional neural network (CNN) model for Afaan Oromoo sentiment analysis based on the architecture developed by (Kim, 2014)

Summary

Introduction

The revolution of web2.0 and the increasing numbers of blogs, social media networks, web reviews, and many others have fundamentally changed the way people express their opinions and share information on the Internet. Convolutional Neural Network for Sentiment Analysis CNN is one of the states of the art deep learning classification algorithm. A multi-channel convolutional neural network for text classification involves using multiple versions of the standard model with different sized kernels This allows the document to be processed at different resolutions or different n-grams (groups of words) at a time, whilst the model learns how to best integrate these interpretations. Pooling layer: In this work max-pooling operation is used as it is extensively used by many researchers the most widely used pooling mechanism In one thing it allows reducing the size of the feature map as it combines the vectors resulting from the different convolutional windows into a single l-dimensional vector and at the same time preserving the most relevant feature. Adam algorithm (Kingma & Ba, 2014) that is a stochastic gradient descent algorithm is used for optimizing parameters of CNN (updating weights)

Long Short Term memory

Forget Gate

Input Gate

Results and Discussion

Conclusions and recommendation