An End-to-End Efficient Lucene-Based Framework of Document/Information Retrieval

In the context of big data and the 4.0 industrial revolution era, enhancing document/information retrieval frameworks efficiency to handle the ever‐growing volume of text data in an ever more digital world is a must. This article describes a double-stage system of document/information retrieval. First, a Lucene-based document retrieval tool is implemented, and a couple of query expansion techniques using a comparable corpus (Wikipedia) and word embeddings are proposed and tested. Second, a retention-fidelity summarization protocol is performed on top of the retrieved documents to create a short, accurate, and fluent extract of a longer retrieved single document (or a set of top retrieved documents). Obtained results show that using word embeddings is an excellent way to achieve higher precision rates and retrieve more accurate documents. Also, obtained summaries satisfy the retention and fidelity criteria of relevant summaries.

Hybrid Query Expansion Model Based on Pseudo Relevance Feedback and Semantic Tree for Arabic IR

In this paper, the authors propose and readapt a new concept-based approach of query expansion in the context of Arabic information retrieval. The purpose is to represent the query by a set of weighted concepts in order to identify better the user's information need. Firstly, concepts are extracted from the initially retrieved documents by the Pseudo-Relevance Feedback method, and then they are integrated into a semantic weighted tree in order to detect more information contained in the related concepts connected by semantic relations to the primary concepts. The authors use the “Arabic WordNet” as a resource to extract, disambiguate concepts and build the semantic tree. Experimental results demonstrate that measure of MAP (Mean Average Precision) is about 10% of improvement using the open source Lucene as IR System on a collection formed from the Arabic BBC news.

QUERY EXPANSION RANKING PADA ANALISIS SENTIMEN MENGGUNAKAN KLASIFIKASI MULTINOMIAL NAÏVE BAYES (Studi Kasus : Ulasan Aplikasi Shopee pada Hari Belanja Online Nasional 2020)

Shopee is one of the e-commerce sites that has many users in Indonesia. Shopee provides various attractive promos on special days such as National Online Shopping Day on December 12. Shopee site was a complete error on December 12, 2020. Complaints and opinions of Shopee users were also shared through various media, one of them was Google Play Store. Sentiment analysis was used to see the user's response to the Shopee’s incident. Sentiment analysis results can be extracted to obtain information regarding positive or negative reviews from Shopee users. Sentiment analysis was performed using the Multinomial Naïve Bayes classification. the simplest method of probability classification, but it is sensitive to feature selection so that the amount of data is determined by the results of feature selection Query Expansion Ranking. The algorithm that has the highest accuracy and kappa statistic is the best algorithm in classifying Shopee’s users sentiment. The results showed that the classification performance using Multinomial Naïve Bayes with 80% of the features (terms) which have the highest Query Expansion Ranking value was obtained at the accuracy and kappa statistics values are 89% and 77.62%. This means that Multinomial Nave Bayes has a good performance in classifying reviews and the number of features used affects the performance results obtained.