Do Sequels Outperform or Disappoint? Insights from an Analysis of Amazon Echo Consumer Reviews

Rapid technological advances in recent years drastically transformed our world. Amidst modern technological inventions such as smart phones, smart watches and smart home devices, consumers of electronic digital devices experience greatly improved automation, productivity, and efficiency in conducting routine daily tasks, information searching, shopping as well as finding entertainment. In the last few years, the global smart speaker market has undergone significant growth. As technology continues to advance and smart speakers are equipped with innovative features, the adoption of smart speakers will increase and so will consumer expectations. This research paper presents an aspect-specific sentiment analysis of consumer reviews of the first three generations of Amazon Echo. Our text mining and aspect-specific sentiment analyses reveal that price, sound, smart home, connectivity, and comparison are outperforming aspects whereas voice, app, Q&A, companionship, and shelf life are disappointing and sunsetting aspects. Our study demonstrates a novel cross-generation visualization of directional changes in consumer sentiment using the Bollinger Bands and volume charts

Social big data analysis of Five Star hotels: A case study of hotel guest experience and satisfaction in Marrakech.

The fast progress of the Internet and mobile devices facilitated the introduction of travel and hospitality review sites, producing a high number (big data) of customer opinion posts. While such comments may manipulate future demand of the specific hotels, they can also be used by hotel managers for bettering customer experience. In order to understand the hotel guest experience and satisfaction in Morocco, especially in Marrakech city during the period from 2015 to 2018, we collected data from “TripAdvisor.com”, the online customer textual reviews, and rating of 7 five star hotels. A Hadoop cluster was configured to handle the social big data extracted, as the method we employed big data analytics and sentiment analysis for handling the sentiment-detection algorithms using tidytext package, as well as bing lexicon. The used techniques were reliable to analyze and identify different characteristics of hotel guest such as “types” of hotel guests; we found that the highest rated type of hotel guests was 'with family'. Concerning the “behavioral”, the type of guest 'with family' seems to comment and expresses his satisfaction or dissatisfaction every month, except in January, September, and December. Moreover, concerning the “nationality” of hotel guests, France represented the highest rate by 70.07%. On the other hand for satisfaction, the “rating” of the field 'service' was decreased in the last two years. Finally, the “sentiment-experience” and the words 'limitations' and 'excellent' were clearly the main hotel attributes mentioned by the customers. The present study showed that big social data analytics and sentiment analysis could be good solutions to help the tourism and hospitality industry to gain insights into the characteristics, satisfaction, and sentiments of the guests to hotels during their experiences at the hotels. However, knowing more elements relevant to the hotel guest would be even more beneficial for hotels in order to enable them to improve their products/services ratio