Analyzing and Filtering Food Items in Restaurant Reviews: Sentiment Analysis and Web Scraping

Within the next few years, sentiment analysis or opinion mining is set to become an important component of real-world applications for product manufacturers, e-commerce companies, and potential customers. Sentiment analysis deals with the computational assessment of people’s opinions apparent or hidden within the text according to three levels: document, sentence and aspect levels. The aspect-level is increasingly becoming an active phase of sentiment analysis. At this level, the aim is to determine the hidden target of opinion represented in datasets, known as aspect term identification. This paper proposes an original hybrid model combining semantic relations and frequency-based approach with supervised classifiers for implicit aspect identification (IAI). The proposed approach is directed towards improving the F1-performances for traditional supervised classifiers commonly used in this field based on eager and lazy learning, and deep learning technique using long short-term memory whit attention mechanism applied for IAI. Particularly, this work addresses aspect term extraction and aggregation, the two sub-tasks of IAI, involving adjectives and verbs. The effects of this approach are empirically examined on multiple datasets of electronic products and restaurant reviews with multiple aspect granularity levels. Comparing this method with similar approaches clearly shows the benefits of this method: (i) the use of an appropriately selected WordNet semantic relations of adjectives and verbs that significantly helps classifiers for IAI. (ii) Using the hybrid model helps classifiers better handle these selected WordNet semantic relations and therefore deal better with IAI.

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Reviews Sentiment analysis for collaborative recommender system

Kurdistan Journal of Applied Research ◽

10.24017/science.2017.3.22 ◽

2017 ◽

Vol 2 (3) ◽

pp. 87-91 ◽

Cited By ~ 2

Author(s):

Alia Karim Abdul Hassan ◽

Ahmed Bahaa Aldeen Abdulwahhab

Keyword(s):

Sentiment Analysis ◽

Recommender System ◽

Classification Model ◽

Additional Source ◽

Data Set ◽

Data Sparsity ◽

Rating Form ◽

Restaurant Reviews ◽

Analysis System ◽

Source Of Information

recommender system nowadays is used to deliver services and information to users. A recommender system is suffering from problems of data sparsity and cold start because of insufficient user rating or absence of data about users or items. This research proposed a sentiment analysis system work on user reviews as an additional source of information to tackle data sparsity problems. Sentiment analysis system implemented using NLP techniques with machine learning to predict user rating form his review; this model is evaluated using Yelp restaurant data set, IMDB reviews data set, and Arabic qaym.com restaurant reviews data set under various classification model, the system was efficient in predicting rating from reviews.

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Aspect Based Mobile Recommendation System

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j9996.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 3840-3846

Keyword(s):

Mobile Phone ◽

Sentiment Analysis ◽

Short Duration ◽

Web Sites ◽

Recommendation System ◽

Text Data ◽

Web Scraping ◽

Hybrid Recommendation

Now-a-days, there is a trend of changing mobile phone models in very short duration. To achieve benefit of choosing a mobile phone satisfying our requirements, mobile phone recommendation system is of great importance. As we know, there are many web sites that provide number of reviews and ratings for each and every mobile phone model available in market. From this, we can understand the consumer opinions and reviews about any mobile product. Existing systems were based on complete review of product as good or bad. But, there is need to have a way with which we can review the product with view of each aspect such as camera, battery, look etc. For this, we have developed a system which provides a recommendation of mobile phone model by considering aspects with user’s choice. For this, we have used reviews, ratings provided by group of users on social website, Amazon. The data is collected with the use of “Octoparse”, a web scraping software and the text data collected is analyzed using Stanford's CoreNLP for sentiment analysis. Our approach provides recommendation, considering user provided aspects (i.e. camera, battery, look etc.) with the use of apache mahout and hybrid recommendation. Our approach showed outstanding performance for mobile phone recommendation

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Summarization and Sentiment Analysis for Financial News

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.38345 ◽

2021 ◽

Vol 9 (10) ◽

pp. 88-90

Author(s):

Anusha Kalbande

Keyword(s):

Stock Market ◽

Sentiment Analysis ◽

Financial Analysts ◽

Relevant Information ◽

Business Leaders ◽

Financial News ◽

Web Scraping ◽

Abstract Data ◽

News Summarization ◽

Online Sources

Abstract: Data is growing at an unimaginable speed around us, but what part of it is really useful information? Business leaders, financial analysts, stock market enthusiasts, researchers etc. often need to go through a plethora of news articles and data every day, and this time spent may not even result in any fruitful insights. Considering such a huge volume of data, there is difficulty in gaining precise, relevant information and interpreting the overall sentiment portrayed by the article. The proposed method helps in conceptualizing a tool that takes financial news from selected and trusted online sources as an input and gives a summary of the same along with a basic positive, negative or neutral sentiment. Here it is assumed that the tool user is familiar with the company’s profile. Based on the input (company name/symbol) given by the user, the corresponding news articles will be fetched using web scraping. All these articles will then be summarized to gain succinct and to the point information. An overall sentiment about the company will be portrayed based on the different important features in the article about the company. Keywords: Financial News; Summarization; Sentiment Analysis.

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