Sentiment Analysis on Customer Responses
- URL: http://arxiv.org/abs/2007.02237v1
- Date: Sun, 5 Jul 2020 04:50:40 GMT
- Title: Sentiment Analysis on Customer Responses
- Authors: Antony Samuels, John Mcgonical
- Abstract summary: We present a customer feedback reviews on product, where we utilize opinion mining, text mining and sentiments.
This research paper provides you with sentimental analysis of various smart phone opinions on smart phones dividing them Positive, Negative and Neutral Behaviour.
- Score: 0.0
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Sentiment analysis is one of the fastest spreading research areas in computer
science, making it challenging to keep track of all the activities in the area.
We present a customer feedback reviews on product, where we utilize opinion
mining, text mining and sentiments, which has affected the surrounded world by
changing their opinion on a specific product. Data used in this study are
online product reviews collected from Amazon.com. We performed a comparative
sentiment analysis of retrieved reviews. This research paper provides you with
sentimental analysis of various smart phone opinions on smart phones dividing
them Positive, Negative and Neutral Behaviour.
Related papers
- Exploring Query Understanding for Amazon Product Search [62.53282527112405]
We study how query understanding-based ranking features influence the ranking process.
We propose a query understanding-based multi-task learning framework for ranking.
We present our studies and investigations using the real-world system on Amazon Search.
arXiv Detail & Related papers (2024-08-05T03:33:11Z) - A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [58.6354685593418]
This paper proposes several article-level, field-normalized, and large language model-empowered bibliometric indicators to evaluate reviews.
The newly emerging AI-generated literature reviews are also appraised.
This work offers insights into the current challenges of literature reviews and envisions future directions for their development.
arXiv Detail & Related papers (2024-02-20T11:28:50Z) - A Comprehensive Review on Sentiment Analysis: Tasks, Approaches and
Applications [0.2717221198324361]
Sentiment analysis (SA) is an emerging field in text mining.
It is the process of computationally identifying and categorizing opinions expressed in a piece of text over different social media platforms.
arXiv Detail & Related papers (2023-11-19T06:29:41Z) - Cross-Domain Consumer Review Analysis [0.0]
The paper presents a cross-domain review analysis on four popular review datasets: Amazon, Yelp, Steam, IMDb.
The analysis is performed using Hadoop and Spark, which allows for efficient and scalable processing of large datasets.
arXiv Detail & Related papers (2022-12-23T18:16:09Z) - SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item
Recommendation [48.1799451277808]
We propose a Sentiment-aware Interactive Fusion Network (SIFN) for review-based item recommendation.
We first encode user/item reviews via BERT and propose a light-weighted sentiment learner to extract semantic features of each review.
Then, we propose a sentiment prediction task that guides the sentiment learner to extract sentiment-aware features via explicit sentiment labels.
arXiv Detail & Related papers (2021-08-18T08:04:38Z) - Can We Automate Scientific Reviewing? [89.50052670307434]
We discuss the possibility of using state-of-the-art natural language processing (NLP) models to generate first-pass peer reviews for scientific papers.
We collect a dataset of papers in the machine learning domain, annotate them with different aspects of content covered in each review, and train targeted summarization models that take in papers to generate reviews.
Comprehensive experimental results show that system-generated reviews tend to touch upon more aspects of the paper than human-written reviews.
arXiv Detail & Related papers (2021-01-30T07:16:53Z) - Are Top School Students More Critical of Their Professors? Mining
Comments on RateMyProfessor.com [83.2634062100579]
Student reviews and comments on RateMyProfessor.com reflect realistic learning experiences of students.
Our study proves that student reviews and comments contain crucial information and can serve as essential references for enrollment in courses and universities.
arXiv Detail & Related papers (2021-01-23T20:01:36Z) - SentiLSTM: A Deep Learning Approach for Sentiment Analysis of Restaurant
Reviews [13.018530502810128]
This paper proposes, a deep learning-based technique (i.e., BiLSTM) to classify the reviews provided by the clients of the restaurant into positive and negative polarities.
The results of the evaluation on test dataset show that BiLSTM technique produced in the highest accuracy of 91.35%.
arXiv Detail & Related papers (2020-11-19T06:24:42Z) - Survey on Visual Sentiment Analysis [87.20223213370004]
This paper reviews pertinent publications and tries to present an exhaustive overview of the field of Visual Sentiment Analysis.
The paper also describes principles of design of general Visual Sentiment Analysis systems from three main points of view.
A formalization of the problem is discussed, considering different levels of granularity, as well as the components that can affect the sentiment toward an image in different ways.
arXiv Detail & Related papers (2020-04-24T10:15:22Z) - Deriving Emotions and Sentiments from Visual Content: A Disaster
Analysis Use Case [10.161936647987515]
Social networks and users' tendency towards sharing their feelings in text, visual and audio content has opened new opportunities and challenges in sentiment analysis.
This article introduces visual sentiment analysis and contrasts it with textual sentiment analysis with emphasis on the opportunities and challenges in this nascent research area.
We propose a deep visual sentiment analyzer for disaster-related images as a use-case, covering different aspects of visual sentiment analysis starting from data collection, annotation, model selection, implementation and evaluations.
arXiv Detail & Related papers (2020-02-03T08:48:52Z) - Teddy: A System for Interactive Review Analysis [17.53582677866512]
Data scientists analyze reviews by developing rules and models to extract, aggregate, and understand information embedded in the review text.
Teddy is an interactive system that enables data scientists to quickly obtain insights from reviews and improve their extraction and modeling pipelines.
arXiv Detail & Related papers (2020-01-15T08:19:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.