Question answering system using web snippets

Menaha, R.; Surya, Anupma; Nandhni, K; M, Ishwarya

doi:10.1109/i-smac.2017.8058377

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…These studies propose classification or learning techniques, where this technique always requires up-to-date training data to maintain the accuracy of the detection. On the other hand, the searching technique to detect hoax news can be done using a snippet as presented in the following studies [12][13][14]. Searching techniques have the advantage of being more up to date and more practical in use.…”

Section:  Issn: 1693-6930mentioning

confidence: 99%

Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization

et al. 2020

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Currently, the spread of hoax news has increased significantly, especially on social media networks. Hoax news is very dangerous and can provoke readers. So, this requires special handling. This research proposed a hoax news detection system using searching, snippet and cosine similarity methods to classify hoax news. This method is proposed because the searching method does not require training data, so it is practical to use and always up to date. In addition, one of the drawbacks of the existing approaches is they are not equipped with a sentiment analysis feature. In our system, sentiment analysis is carried out after hoax news is detected. The goal is to extract the true hidden sentiment inside hoax whether positive sentiment or negative sentiment. In the process of sentiment analysis, the Naïve Bayes (NB) method was used which was optimized using the Particle Swarm Optimization (PSO) method. Based on the results of experiment on 30 hoax news samples that are widely spread on social media networks, the average of hoax news detection reaches 77% of accuracy, where each news is correctly identified as a hoax in the range between 66% and 91% of accuracy. In addition, the proposed sentiment analysis method proved to has a better performance than the previous analysis sentiment method.

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Section:  Issn: 1693-6930mentioning

confidence: 99%

Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization

et al. 2020

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“…Based on the domain of question answered by the QA system can be classified as open-domain QA or closed-domain QA. Closed-domain QA answer question to a specific field, whereas an open domain QA gives the user the freedom to raise queries related to any topic [4]. The QA system proposed for the travel domain in [5] can be a suitable example for closed-domain QA, whereas the QA system developed for factoid-based question [what, when, who, which, how] is an open-domain QA [6].…”

Section: Introductionmentioning

confidence: 99%

Chatbot Analytics Based on Question Answering System and Deep Learning: Case Study for Movie Smart Automatic Answering

Shah¹,

Mohammed²

2020

IJSEIA

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Question Answer (QA) systems are established to retrieves accurate and concise answers to human queries posted in natural language. The primary focus of the QA system is to achieve efficient and natural interaction between machines and humans. To achieve the above several researchers are directed towards Natural Language Processing (NLP) based deep learning. With the rise of a variety of deep NLP models, it is now possible to obtain a vector form of words and sentences that stores the meaning of the context. NLP considerably aids deep learning-based mathematical models in understanding the semantic and syntax of natural human language. The Cornell Movie-Dialogs Corpus created at Cornell University, and Movie Dialog Dataset created at Facebook are preprocessed and used to train the chatbot. Deep learning model has been built to answer questions about movies from Moview reviews. The encoder and decoder of the Seq2Seq model comprise of LSTM cells and are defined using Bidirectional Dynamic RNN and Dynamic Decoder RNN package of the tensor flow library. Additionally, to ensure the chatbot performs well on long sentences attention mechanism from the tensor flow library is applied to the decoder. In this paper, research is conducted on build a smart chatbots based QA system that employs a deep learning model. The deep learning model employs a sequence-to-sequence (Seq2Seq) word embedding that was proposed by Ilya Sutskever in 2014, which had laid the foundation for building chatbot model build in this paper.

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Question answering system using web snippets

Cited by 2 publications

References 6 publications

Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization

Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization

Chatbot Analytics Based on Question Answering System and Deep Learning: Case Study for Movie Smart Automatic Answering

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