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Social media has become the main venue for users to express their opinions and feelings, generating a vast number of available and valuable data to be scrutinized by researchers and marketers. This paper aims to extend previous studies analyzing social media reviews through text mining and sentiment analysis to provide useful recommendations for management in the restaurant industry.

The Lexalytics, a text mining artificial intelligence tool, is applied to analyze the text of the online reviews of the restaurants in a touristic Dutch village extracted from the most frequently used social media platforms focusing on the four restaurant quality factors, namely, food and beverage, service, atmosphere and value.

The findings of this research are presented by the identified key themes with comparisons of the customers’ review sentiment between a selected restaurant, Zwaantje, vis-à-vis its bench-mark restaurants set by a specific approach under the abovementioned quality dimensions, in which the food and beverage and service are the most commented by customers. Results demonstrate that text mining can generate insights from different aspects and that the proposed approach is valuable to restaurant management.

The paper provides a relatively big scale in numbers and resources of social media reviews to further explore the most important service dimensions in the restaurant industry in a specific tourist area. It also offers a useful framework to apply the text mining business intelligence tool by comparison of peers for local small business restaurant practitioners to improve their management skills beyond manually reading social media reviews.

Hate speech in recent times has become a troubling development. It has different meanings to different people in different cultures. The anonymity and ubiquity of the social media provides a breeding ground for hate speech and makes combating it seems like a lost battle. However, what may constitute a hate speech in a cultural or religious neutral society may not be perceived as such in a polarized multi-cultural and multi-religious society like Nigeria. Defining hate speech, therefore, may be contextual. Hate speech in Nigeria may be perceived along ethnic, religious and political boundaries. The purpose of this paper is to check for the presence of hate speech in social media platforms like Twitter, and to what degree is hate speech permissible, if available? It also intends to find out what monitoring mechanisms the social media platforms like Facebook and Twitter have put in place to combat hate speech. Lexalytics is a term coined by the authors from the words lexical analytics for the purpose of opinion mining unstructured texts like tweets.

This research developed a Python software called polarized opinions sentiment analyzer (POSA), adopting an ego social network analytics technique in which an individual’s behavior is mined and described. POSA uses a customized Python N-Gram dictionary of local context-based terms that may be considered as hate terms. It then applied the Twitter API to stream tweets from popular and trending Nigerian Twitter handles in politics, ethnicity, religion, social activism, racism, etc., and filtered the tweets against the custom dictionary using unsupervised classification of the texts as either positive or negative sentiments. The outcome is visualized using tables, pie charts and word clouds. A similar implementation was also carried out using R-Studio codes and both results are compared and a t-test was applied to determine if there was a significant difference in the results. The research methodology can be classified as both qualitative and quantitative. Qualitative in terms of data classification, and quantitative in terms of being able to identify the results as either negative or positive from the computation of text to vector.

The findings from two sets of experiments on POSA and R are as follows: in the first experiment, the POSA software found that the Twitter handles analyzed contained between 33 and 55 percent hate contents, while the R results show hate contents ranging from 38 to 62 percent. Performing a t-test on both positive and negative scores for both POSA and R-studio, results reveal p-values of 0.389 and 0.289, respectively, on an α value of 0.05, implying that there is no significant difference in the results from POSA and R. During the second experiment performed on 11 local handles with 1,207 tweets, the authors deduce as follows: that the percentage of hate contents classified by POSA is 40 percent, while the percentage of hate contents classified by R is 51 percent. That the accuracy of hate speech classification predicted by POSA is 87 percent, while free speech is 86 percent. And the accuracy of hate speech classification predicted by R is 65 percent, while free speech is 74 percent. This study reveals that neither Twitter nor Facebook has an automated monitoring system for hate speech, and no benchmark is set to decide the level of hate contents allowed in a text. The monitoring is rather done by humans whose assessment is usually subjective and sometimes inconsistent.

This study establishes the fact that hate speech is on the increase on social media. It also shows that hate mongers can actually be pinned down, with the contents of their messages. The POSA system can be used as a plug-in by Twitter to detect and stop hate speech on its platform. The study was limited to public Twitter handles only. N-grams are effective features for word-sense disambiguation, but when using N-grams, the feature vector could take on enormous proportions and in turn increasing sparsity of the feature vectors.

The findings of this study show that if urgent measures are not taken to combat hate speech there could be dare consequences, especially in highly polarized societies that are always heated up along religious and ethnic sentiments. On daily basis tempers are flaring in the social media over comments made by participants. This study has also demonstrated that it is possible to implement a technology that can track and terminate hate speech in a micro-blog like Twitter. This can also be extended to other social media platforms.

This study will help to promote a more positive society, ensuring the social media is positively utilized to the benefit of mankind.

The findings can be used by social media companies to monitor user behaviors, and pin hate crimes to specific persons. Governments and law enforcement bodies can also use the POSA application to track down hate peddlers.

With the growing popularity of social media, it has become common practice for consumers to write online reviews to share their opinion and experience as well as consider others' reviews to inform purchase decision-making. This study investigated how online review sentiments towards four key aspects (food, service, ambience and price) change after a restaurant is awarded a Michelin Star to shed light on how the award of a Michelin Star affects online reviews as well as what factors contribute to positive online restaurant reviews.

The authors conducted a sentiment analysis of online restaurant reviews on TripAdvisor. A total of 8,871 English-written reviews from 87 restaurants located in Europe were extracted using a web crawler developed by Beautiful Soup, and data were then processed using Semantria.

The study findings revealed that overall sentiments decreased after restaurants were awarded a Michelin Star, in which service sentiment was the most affected aspect, followed by food and ambience. Yet, price sentiment showed a prominent increase. This provides valuable insights for Michelin-starred restaurant operators and owners to create a unique and compelling gastronomic experience that triggers positive online reviews.

The results of this study argue that consumers tend to hold higher expectations for this type of upscale restaurants given its recognition and quality assurance, so they are more likely to have negative feelings when their expectations are disconfirmed. Therefore, restaurants should continuously improve their food and service while paying attention to small details such as ambience, through creativity and innovation. Also, high-end restaurants, especially Michelin-starred restaurants, usually have the edge in premium pricing, yet competitive pricing may backfire considering its perceived luxurious values.

This study analyzed changes in customer sentiments when a restaurant is awarded a Michelin Star through text analytics. Through the lens of online restaurant reviews, the study findings contribute to identifying aspects that are most or least affected by the award of a Michelin Star as well as highlight the role of ambience in customer satisfaction which might have been overlooked in previous studies.

隨著社交媒體日趨普及，消費者出現一種常見的做法，就是在網上書寫評論，分享他們的意見和體驗，他們也會參考其他消費者的評論，以在購物時能作出知情決定。本研究擬探討當餐館獲得米其林星級時，消費者對它們在四個主要方面 (即食物、服務、情調和價格) 的網上評價會如何改變。我們藉此能更容易了解、餐館獲得米其林星級會如何影響其網上評論，以及是哪些因素、會為這些餐館帶來正面的網上評價。

我們對貓途鷹平台上的網上餐館評論進行情感分析。透過BeautifulSoup 研發的網絡爬蟲，我們取出位於歐洲87間餐館、共8,871個以英文書寫的評論，並把這些數據以Semantria加以處理。

研究結果顯示、當餐館獲得米其林星級時，顧客的整體情緒會下降，而其中最受影響的是服務情懷，其次是食物和情調; 但價格情緒卻有明顯的上升。這研究結果給獲得米其林星級餐館的經營者及其東主提供寶貴的啟示，讓他們了解如何為顧客創造一個可帶來正面網上評價的獨特而難忘的美食體驗。

本研究透過文本挖掘、去分析當餐館獲得米其林星級時，顧客情緒會如何改變; 透過網上餐館評論這面透視鏡子，本研究得到的結果、幫助我們確定米其林星級的聲譽所影響最大和最小的是哪些方面，以及讓我們更深入了解餐館的情調在顧客滿意程度上所扮演的角色，而這個角色在過去的研究中似被忽視。

本研究的結果提供了論據、證明由於消費者對擁有相關的認可和品質保證的這類高檔餐館一般予以較高的期望，故當他們發現期望與現實不符時，他們更容易產生負面的情緒; 因此，餐館在關注如情調方面的細節的同時，也應透過創造力和新觀念、去不斷改善他們提供的食物素質和服務水平; 而且，高檔餐館，尤其是獲得米其林星級的餐館，通常在溢價定價方面享有優勢，但當考慮到感知的奢華價值時，具競爭力的價格或會為餐館帶來反效果。

Customer knowledge from social media can become an important organizational asset. The purpose of this paper is to identify useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers from social media data and facilitate social media-based customer knowledge management.

The authors conducted a case study to analyze people’s online discussion on Twitter regarding laptop brands and manufacturers. After collecting relevant tweets using Twitter search APIs, the authors applied statistical analysis, text mining and sentiment analysis techniques to analyze the social media data set and visualize relevant insights and patterns in order to identify customer knowledge.

The paper identifies useful insights and knowledge from customers and knowledge about customers from social media data. Furthermore, the paper shows how the authors can use knowledge from customers and knowledge about customers to help companies develop knowledge for customers.

This is an original social media analytics study that discusses how to transform large-scale social media data into useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers.

The increasing competition among higher education institutions (HEI) has led students to conduct a more in-depth analysis to choose where to study abroad. Since students are usually unable to visit each HEIs before making their decision, they are strongly influenced by what is written by former international students (IS) on the internet. HEIs also benefit from such information online. The purpose of this paper is to provide an understanding of the drivers of HEIs success online.

Due to the increasing amount of information published online, HEIs have to use automatic techniques to search for patterns instead of analysing such information manually. The present paper uses text mining (TM) and sentiment analysis (SA) to study online reviews of IS about their HEIs. The paper studied 1938 reviews from 65 different business schools with Association to Advance Collegiate Schools of Business accreditation.

Results show that HEIs may become more attractive online if they financially support students cost of living, provide courses in English, and promote an international environment.

Despite the use of a major platform with a broad number of reviews from students around the world, other sources focussed on other types of HEIs may have been used to reinforce the findings in the current paper.

The study pioneers the use of TM and SA to highlight topics and sentiments mentioned in online reviews by students attending HEIs, clarifying how such opinions are correlated with satisfaction. Using such information, HEIs’ managers may focus their efforts on promoting international attractiveness of their institutions.

Social media analytics uses data mining platforms, tools and analytics techniques to collect, monitor and analyze massive amounts of social media data to extract useful patterns, gain insight into market requirements and enhance business intelligence. The purpose of this paper is to propose a framework for social media competitive intelligence to enhance business value and market intelligence.

The authors conducted a case study to collect and analyze a data set with nearly half million tweets related to two largest retail chains in the world: Walmart and Costco in the past three months during December 1, 2014-February 28, 2015.

The results of the case study revealed the value of analyzing social media mentions and conducting sentiment analysis and comparison on individual product level. In addition to analyzing the social media data-at-rest, the proposed framework and the case study results also indicate that there is a strong need for creating a social media data application that can conduct real-time social media competitive intelligence for social media data-in-motion.

So far there is little research to guide businesses for social media competitive intelligence. This paper proposes a novel framework for social media competitive intelligence to illustrate how organizations can leverage social media analytics to enhance business value through a case study.

This paper aims to equip professionals and researchers in the fields of advertising, branding, public relations, marketing communications, social media analytics and marketing with a simple, effective and dynamic means of evaluating consumer behavioural sentiments and engagement through Arabic language and script, in vivo.

Using quantitative and qualitative situational linguistic analyses of Classical Arabic, found in Quranic and religious texts scripts; Modern Standard Arabic, which is commonly used in formal Arabic channels; and dialectical Arabic, which varies hugely from one Arabic country to another: this study analyses rich marketing and consumer messages (tweets) – as a basis for developing an Arabic language social media methodological tool.

Despite the popularity of Arabic language communication on social media platforms across geographies, currently, comprehensive language processing toolkits for analysing Arabic social media conversations have limitations and require further development. Furthermore, due to its unique morphology, developing text understanding capabilities specific to the Arabic language poses challenges.

This study demonstrates the application and effectiveness of the proposed methodology on a random sample of Twitter data from Arabic-speaking regions. Furthermore, as Arabic is the language of Islam, the study is of particular importance to Islamic and Muslim geographies, markets and marketing.

The findings suggest that the proposed methodology has a wider potential beyond the data set and health-care sector analysed, and therefore, can be applied to further markets, social media platforms and consumer segments.

To remedy these gaps, this study presents a new methodology and analytical approach to investigating Arabic language social media conversations, which brings together a multidisciplinary knowledge of technology, data science and marketing communications.

By doing sentiment analysis of netnographic data, this study aims to explain the need to give special attention to negative sentiments expressed in virtual tribes, as they play a significant role in translating the informational mode of conversation to the relational mode of conversation. The overall purpose is to aid brand managers in the process of brand co-creation by articulating brand communication targeted to specific audiences based on their shared passions and interests.

The study adopts a mixed-methods approach. The primary data were collected from two virtual brand communities through qualitative net-based ethnographic methods. Semantria Excel plug-in was used to categorize the extracted consumer statements based on positive, neutral and negative sentiments.

Managing the negative interactions in the virtual communities and relationship development with members through non-commercial conversations should be the two main priorities for effective brand management. Sentiment analysis specifically helps to identify pain points and consumer sentiments at each stage of the shopper journey. The findings of the study endorse the importance of offering and supporting communities as a valid marketing.

This paper shows how systematic attention to user interactions on virtual brand communities can be used for tribal marketing, which in turn will impact the intangible aspects of the business, such as increasing brand value and loyalty. By engaging the consumers, the social ties among the target audience can be nurtured and strengthened.

This paper focuses on decoding their behavior by unpeeling the consumer statements rather than tangible aspects of the business, such as sales of products or services. It contributes to development of a theoretical framework that outlines how the interactions in virtual brand communities can aid in formulating the functional and communicational strategies for a brand.