Listening at Scale: A Replicable, Low-Cost Pipeline for Collecting and Analyzing Google Business Reviews with SerpApi and VADER
DOI:
https://doi.org/10.47743/saeb-2026-0027Keywords:
sentiment analysis, VADER, SerpApi, Google Maps review, SMEsAbstract
Small and medium-sized enterprises increasingly rely on public review platforms as signals of quality, reputation, and customer experience, yet many owners still lack a practical way to process multilingual review text at regular intervals. This study develops a low-cost and reproducible workflow for collecting Google Business reviews, filtering translated English content, and summarizing sentiment with open analytical tools. The pipeline uses SerpApi to retrieve recent reviews for a known Google Maps place identifier and VADER to classify translated review text into positive, neutral, and negative sentiment. The procedure is demonstrated on 200 Google reviews for an auto-rental location, Klass Wagen Sibiu. Results indicate a strongly positive corpus, with 85% positive, 4% neutral, and 11% negative reviews, a mean VADER compound score of 0.538, and a median of 0.733. The association between VADER scores and star ratings is high, r = 0.83, but it is interpreted as convergent descriptive evidence rather than classification accuracy. The study contributes an applied decision-support blueprint that connects online-review analytics, sentiment analysis, and SME monitoring practice. Its limits remain important: review data are self-selected, platform access is constrained, automatic translation can alter sentiment, and lexicon-based models may miss sarcasm or domain-specific wording.
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Copyright (c) 2026 Marian Pompiliu Cristescu, Dumitru Alexandru Mara, Ana-Maria Constantinescu, Ioana Petrea, Ana Englitera Visan
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