(IEEE 2021) User Sentiment Analysis in the Fintech OVO Review Based on the Lexicon Method

User reviews are important in the new approach to fintech services. To learn this information, a simple sentiment analysis can make the right observations to support the OVO fintech system in analyzing the success of the fintech system. The analysis has several stages, starting from how to extract comment data from the play store, extracting meaningful information from the play store platform, and extracting the data into valuable information. Moreover, accurate topic modeling and document representation is another challenging task in sentiment analysis. We propose a lexicon-based topic modeling in observing user sentiment simply by looking at the number of words that appear. The proposed system retrieves OVO fintech comment data from the Play Store, removes irrelevant content to extract meaningful information, and generates topics and features from the extracted data using NLTK. Data processing using google collab in Python language where data is used freely. Data analysis using the word cloud method, Exploratory Data Analysis (EDA), correlation analysis between words, ordering the number of words in sentences revealed that OVO comments in that period tended to be negativ

User Sentiment Analysis in the Fintech OVO Review Based on the Lexicon Method

Abstract—User reviews are important in the new approach to fintech services. To learn this information, a simple sentiment analysis can make the right observations to support the OVO fintech system in analyzing the success of the fintech system.The analysis has several stages, starting from how to extract comment data from the play store, extracting meaningful information from the play store platform, and extracting the data into valuable information. Moreover, accurate topic modeling and document representation is another challenging task in sentiment analysis. We propose a lexicon-based topic modeling in observing user sentiment simply by looking at the number of words that appear. The proposed system retrieves OVO fintech comment data from the Play Store, removes irrelevant content to extract meaningful information, and generates topics and features from the extracted data using NLTK. Data processing using google collab in Python language where data is used freely. Data analysis using the word cloud method, Exploratory Data Analysis (EDA), correlation analysis between words, ordering the number of words in sentences revealed that OVO comments in that period tended to be negativ

Dual memory network model for sentiment analysis of review text

In sentiment analysis of product reviews, both user and product information are proven to be useful. Current works handle user profile and product information in a unified model which may not be able to learn salient features of users and products effectively. In this work, we propose a dual user and product memory network (DUPMN) model to learn user profiles and product information for reviews classification using separate memory networks. Then, the two representations are used jointly for sentiment analysis. The use of separate models aims to capture user profiles and product information more effectively. Comparing with state-of-the-art unified prediction models, evaluations on three benchmark datasets (IMDB, Yelp13, and Yelp14) show that our dual learning model gives performance gain of 0.6%, 1.2%, and 0.9%, respectively. The improvements are also deemed very significant measured by p-values