Framework to Analyze Customer’s Feedback in Smartphone Industry Using Opinion Mining

Gupta, Mayank; Sebastian, Shoney

doi:10.11591/ijece.v8i5.pp3317-3324

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…The proposed system uses the standard dataset of product review data from Amazon that is frequently used for sentiment analysis [31]. The format of the data is reconfirmed to be taking the shape of big data by referring publically available big data [32].…”

Section: Results Analysismentioning

confidence: 99%

Framework for opinion as a service on review data of customer using semantics based analytics

Rajeshwari¹,

Vagdevi²

2020

IJECE

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At Opinion mining plays a significant role in representing the original and unbiased perception of the products/services. However, there are various challenges associated with performing an effective opinion mining in the present era of distributed computing system with dynamic behaviour of users. Existing approaches is more laborious towards extracting knowledge from the reviews of user which is further subjected to various rounds of operation with complex procedures. The proposed system addresses the problem by introducing a novel framework called as Opinion-as-a-Service which is meant for direct utilization of the extracted knowledge in most user friendly manner. The proposed system introduces a set of three sequential algorithm that performs aggregated of incoming stream of opinion data, performing indexing, followed by applying semantics for extracting knowledge. The study outcome shows that proposed system is better than existing system in mining performance.

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Section: Results Analysismentioning

confidence: 99%

Framework for opinion as a service on review data of customer using semantics based analytics

Rajeshwari¹,

Vagdevi²

2020

IJECE

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“…Other studies analyzed customer's opinions rather than their behavior to understand their demands. Another study analyzed customers' opinions rather than their behavior to understand their demands [19].…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient enhanced k-means clustering algorithm for best offer prediction in telecom

Fraihat

Awad

et al. 2022

IJECE

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<span>Telecom companies usually offer several rate plans or bundles to satisfy the customers’ different needs. Finding and recommending the best offer that perfectly matches the customer’s needs is crucial in maintaining customer loyalty and the company’s revenue in the long run. This paper presents an effective method of detecting a group of customers who have the potential to upgrade their telecom package. The used data is an actual dataset extracted from call detail records (CDRs) of a telecom operator. The method utilizes an enhanced k-means clustering model based on customer profiling. The results show that the proposed k-means-based clustering algorithm more effectively identifies potential customers willing to upgrade to a higher tier package compared to the traditional k-means algorithm. Our results showed that our proposed clustering model accuracy was over 90%, while the traditional k-means accuracy was under 70%.</span>

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“…Their work [18] was based on a sentiment analysis which they referred to as opinion mining. They come up with a new idea to analyzing customer feedback from Amazon.…”

Section: Related Workmentioning

confidence: 99%

Reputation Measurement based on a Hybrid Sentiment Analysis Approach for Saudi Telecom Companies

Abdullah¹,

Alosaimi²,

Almotiri³

2021

IJACSA

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Thousands of active people on social media daily share their thoughts and opinions about different subjects and different issues. Many social media platforms used to express the feeling or opinion and at top of it is Twitter. On Twitter, many opinions are expressed in many fields such as movies, events, products, and services; this data considered a valuable resource for companies and decision-makers to help in making decisions. This study was based on using a hybrid approach to extract the opinions from an Arabic tweet to measuring service providers' reputation. In this study, the Saudi telecom companies used as a case study. This research concentrates on determining peoples' opinions more accurately by utilizing the Retweet and Favorite. The number resulting from positive and negative tweets after applying the polarity equation was used to estimate reputation scores. The result indicated that the STC company represents a high reputation compared to other companies. The proposed approach shows promising results to expand existing knowledge of sentiment analysis in the domain of measure reputation.

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Framework to Analyze Customer’s Feedback in Smartphone Industry Using Opinion Mining

Cited by 6 publications

References 18 publications

Framework for opinion as a service on review data of customer using semantics based analytics

Framework for opinion as a service on review data of customer using semantics based analytics

An efficient enhanced k-means clustering algorithm for best offer prediction in telecom

Reputation Measurement based on a Hybrid Sentiment Analysis Approach for Saudi Telecom Companies

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