Reported details
What happened
The record is titled "Sentiment Analysis of Online Customer Reviews to Optimize Marketing Strategies in the E-Commerce Industry" and lists Mohammed Arvaz and Dr. Richa Das as authors.
It identifies the International Journal for Research in Applied Science and Engineering Technology as the venue and gives 2026 as the publication year.
The work has DOI 10.22214/ijraset.2026.84706 and OpenAlex ID W7204457502.
The supplied metadata marks publication status as unknown; the record therefore does not establish whether the work is peer-reviewed.
Business relevance
Why it matters
Review sentiment is a potentially useful input for merchants deciding which customer concerns deserve attention, but this record alone does not show that a particular sentiment method improves marketing performance.
The title connects customer feedback with marketing strategy, making the item relevant to teams that already collect review text and want to explore structured analysis.
Because only bibliographic metadata is available, the immediate value is as a research lead rather than evidence for changing budgets, targeting, or messaging.
Editorial perspective
Analysis & judgment
The safest reading is that this is a discovery signal about a research topic, not a validated operating recommendation. No methods, sample, results, or causal claims are available in the supplied evidence.
Merchants considering sentiment workflows should distinguish between classifying positive or negative language and deciding what action to take. A label may help organize review work, but it does not by itself establish customer priorities, commercial impact, or the right response.
The publication-status field is unknown, so teams should verify the record and assess the full work before treating it as research support. Until then, use it to frame questions for a controlled internal review process rather than as proof that sentiment analysis will optimize marketing.
Applicability
Seller impact
If a seller already has searchable review data, the record may justify a limited investigation into whether sentiment categories help surface recurring product, service, or messaging issues.
If review data is sparse, inconsistent, or difficult to export, the practical constraint is likely data quality and workflow fit rather than the existence of a new research reference.
Marketing teams should not attribute revenue, conversion, or retention changes to sentiment analysis based on this record, because those outcomes are not reported in the available evidence.
Action plan
What to do now
- 1
Log the research lead
nowSave the DOI and OpenAlex ID in the team’s research tracker, and label the item publication status as unknown rather than peer-reviewed or unreviewed.
- 2
Map the available review data
this-weekCheck whether review text can be grouped consistently by product, issue, date, and channel. Proceed with a small exploratory workflow only if those fields are available and usable.
- 3
Define a decision before testing sentiment
this-weekChoose one operational question—such as prioritizing review responses or identifying recurring complaints—and specify what evidence would count as useful before adopting a sentiment tool.
- 4
Verify the underlying publication
monitorLook for the publisher record or full research text before citing findings, methods, or performance claims. Do not escalate the item into a marketing decision while its publication status and substantive content remain unverified.
What not to do yet
- Do not claim that the work proves sentiment analysis improves e-commerce marketing, conversion, or revenue.
- Do not describe the item as peer-reviewed when its supplied publication status is unknown.
- Do not infer methods, accuracy, sample size, findings, or quotations from the title and bibliographic metadata alone.
Sources & context
Evidence and sources
A source may provide only a headline or summary. Read the evidence scope below. Links and workflow checks are not independent fact verification. Internal confidence values are workflow signals, not probabilities of factual correctness.
- 01Source link
Sentiment Analysis of Online Customer Reviews to Optimize Marketing Strategies in the E-Commerce Industry — unknown — International Journal for Research in Applied Science and Engineering Technology
OpenAlex Ecommerce Research Discovery · Workflow status: single-source
Retrieved: August 29, 2026 at 04:17 a.m. UTC
Bibliographic metadata only; no body, abstract, quotation, or PDF is retained.