Sentiment Analysis of Restaurant Customer Reviews on TripAdvisor using Naïve Bayes

Laksono, Rachmawan Adi; Sungkono, Kelly Rossa; Sarno, Riyanarto; Wahyuni, Cahyaningtyas Sekar

doi:10.1109/icts.2019.8850982

Cited by 77 publications

(40 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this testing scenario, a meat classification trial was conducted using the Naïve Bayes method with 5 variations of meat with 3 different temperature variations. Naive Bayes calculates destiny possibility predictions from facts or studies that had been given, based at the opportunity point ofview [22]. In the testing of scenario 3, k-fold cross-validation was used, with k = 10 as shown in Table 4.…”

Section: Scenario 3 Testing (Naïve Bayes Method)mentioning

confidence: 99%

Temperature effect of electronic nose sampling for classifying mixture of beef and pork

Laga

Sarno

2020

IJEECS

Self Cite

View full text Add to dashboard Cite

Strong demand and strong price of raw foodstuffs like beef was commonly used in conventional markets by beef dealers to commit fraud in order to gain larger income. The fraud has been in the form of combining beef and pork. In Indonesia, this has been a issue of food health in recent years. Via scent, some food safety concerns can be expected. By using electronic nose that is equipped with electrochemical and air sensors such as temperature sensors, strain, and humidity to find the pure beef or mixed beef. According to its selectivity, the sensor can detect gas to make small icurrents that are the result of chemical sensor and gas interactions with oxygen .In this study, the classification method k-NN, SVM, Naïve Bayes, and Random Forest was used in 5 different meat variations with a ratio of 0%, 10%, 50%, 90% and 100% with temperatures of -22°C, Room Temp., And 55 ° C. The results showed the effect of temperature on increasing the accuracy, which is at a temperature of -22 ° C. The lower the temperature, the more stable the value obtained by electronic nose. At a temperature of -22 ° C, the method that produces the highest accuracy is the Random Forest method.

show abstract

Section: Scenario 3 Testing (Naïve Bayes Method)mentioning

confidence: 99%

Temperature effect of electronic nose sampling for classifying mixture of beef and pork

Laga

Sarno

2020

IJEECS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Pre-processing steps, which included removing URLs, numbers or stop-words, did not have any effect on the performance of the sentiment classifier. In addition, Sentiment Analysis on Twitter has also been applied to extract restaurant reviews from the Yelp 1 and TripAdvisor 2 datasets [22,23]. Sentiment analysis on social media may be used for novel applications such as analysing the effect of a celebrity's endorsement of products [24], identifying human trafficking [25], and education [26].…”

Section: Sentiment Analysismentioning

confidence: 99%

Tweeting about twenty: an analysis of interest, public sentiments and opinion about 20mph speed restrictions in two UK cities

et al. 2021

View full text Add to dashboard Cite

Background Twenty miles per hour (20mph) speed limits (equivalent to roughly 30kmh) have become part of public health policies to reduce urban road collisions and casualties, especially in Western countries. Public opinion plays a crucial role in opposition to and acceptance of policies that are advocated for improving public health. Twenty miles per hour speed limit policies were implemented in Edinburgh and Belfast from 2016 to 2018. In this paper, we extract public opinion and sentiments expressed about the new 20mph speed limits in those cities using publicly available Twitter data. Methods We analysed public sentiments from Twitter data and classified the public comments in plain English into the categories ‘positive’, ‘neutral’, and ‘negative’. We also explored the frequency and sources of the tweets. Results The total volume of tweets was higher for Edinburgh than for Belfast, but the volume of tweets followed a similar pattern, peaking around 2016, which is when the schemes were implemented. Overall, the tone of the tweets was positive or neutral towards the implementation of the speed limit policies. This finding was surprising as there is a perception among policymakers that there would have been public backlash against these sorts of policy changes. The commonly used hashtags focused largely on road safety and other potential benefits, for example to air pollution. Conclusions Overall, public attitudes towards the policies were positive, thus policymakers should be less anxious about potential public backlash when considering the scale-up of 20mph speed restrictions.

show abstract

“…These results demonstrated a higher level of performance in comparison with the other sentiment analysis methods (Fikri and Sarno, (2019) that used SentiWordNet, a public dictionary and the support vector machine algorithm, as a complex machine-learning algorithm with a maximum accuracy of 76%). This provides explicit evidence of the better performance of our proposed bag-of-words selection process in comparison to the other sentiment analysis methods (see also the accuracy metrics of sentiment analysis models provided by Priyantina and Sarno (2019); Laksono et al (2019); Billyan et al (2019)).…”

Section: Conclusion and Discussion Of The Findingsmentioning

confidence: 69%

A generalizable sentiment analysis method for creating a hotel dictionary: using big data on TripAdvisor hotel reviews

Bagherzadeh

Shokouhyar

Jahani

et al. 2021

JHTT

View full text Add to dashboard Cite

Purpose Research analyzing online travelers’ reviews has boomed over the past years, but it lacks efficient methodologies that can provide useful end-user value within time and budget. This study aims to contribute to the field by developing and testing a new methodology for sentiment analysis that surpasses the standard dictionary-based method by creating two hotel-specific word lexicons. Design/methodology/approach Big data of hotel customer reviews posted on the TripAdvisor platform were collected and appropriately prepared for conducting a binary sentiment analysis by developing a novel bag-of-words weighted approach. The latter provides a transparent and replicable procedure to prepare, create and assess lexicons for sentiment analysis. This approach resulted in two lexicons (a weighted lexicon, L1 and a manually selected lexicon, L2), which were tested and validated by applying classification accuracy metrics to the TripAdvisor big data. Two popular methodologies (a public dictionary-based method and a complex machine-learning algorithm) were used for comparing the accuracy metrics of the study’s approach for creating the two lexicons. Findings The results of the accuracy metrics confirmed that the study’s methodology significantly outperforms the dictionary-based method in comparison to the machine-learning algorithm method. The findings also provide evidence that the study’s methodology is generalizable for predicting users’ sentiment. Practical implications The study developed and validated a methodology for generating reliable lexicons that can be used for big data analysis aiming to understand and predict customers’ sentiment. The L2 hotel dictionary generated by the study provides a reliable method and a useful tool for analyzing guests’ feedback and enabling managers to understand, anticipate and re-actively respond to customers’ attitudes and changes. The study also proposed a simplified methodology for understanding the sentiment of each user, which, in turn, can be used for conducting comparisons aiming to detect and understand guests’ sentiment changes across time, as well as across users based on their profiles and experiences. Originality/value This study contributes to the field by proposing and testing a new methodology for conducting sentiment analysis that addresses previous methodological limitations, as well as the contextual specificities of the tourism industry. Based on the paper’s literature review, this is the first research study using a bag-of-words approach for conducting a sentiment analysis and creating a field-specific lexicon.

show abstract

Sentiment Analysis of Restaurant Customer Reviews on TripAdvisor using Naïve Bayes

Cited by 77 publications

References 16 publications

Temperature effect of electronic nose sampling for classifying mixture of beef and pork

Temperature effect of electronic nose sampling for classifying mixture of beef and pork

Tweeting about twenty: an analysis of interest, public sentiments and opinion about 20mph speed restrictions in two UK cities

A generalizable sentiment analysis method for creating a hotel dictionary: using big data on TripAdvisor hotel reviews

Contact Info

Product

Resources

About