As a method of determining what types of social media messages work best for hospitality firms, this study examined what types of messages gained the most clicks of “Like” and comments on Facebook. An analysis of the number of likes and comments regarding nine hundred and eighty-two Facebook messages from ten restaurant chains and two independent operators revealed clear patterns. The more popular keywords involved information about the restaurant (e.g., menu descriptions) and the less popular messages were those that contained marketing-related words (including “winner” and “check”). Dividing the messages into four media types, namely, status (text only), link (containing a URL), video (embedding a video), and photo (showing photos), revealed that photo and status receive more likes and comments than the other two categories. Social media messages can also be categorized into two message types: sales and marketing (about two-thirds of the messages in this study) and conversational messages. Based on number of likes and comments, conversational messages are endorsed by more Facebook users. Finally, cross-effects of media type and message type affect the number of comments a message received. Although these results do not expressly assess Facebook users’ reactions, the guidelines developed here should help managers improve their use of Facebook, as well as provide groundwork for developing a defined typology of Facebook messages and an automatic text classifier with the machine learning techniques.

In this article, we discuss the design of party classifiers for Congressional speech data. We then examine these party classifiers' person-dependency and time-dependency. We found that party classifiers trained on 2005 House speeches can be generalized to the Senate speeches of the same year, but not vice versa. The classifiers trained on 2005 House speeches performed better on Senate speeches from recent years than on older ones, which indicates the classifiers' time-dependency. This dependency may be caused by changes in the issue agenda or the ideological composition of Congress.

This article presents an empirical evaluation of text classification methods in literary domain. This study compared the performance of two popular algorithms, naïve Bayes and support vector machines (SVMs) in two literary text classification tasks: the eroticism classification of Dickinson's poems and the sentimentalism classification of chapters in early American novels. The algorithms were also combined with three text pre-processing tools, namely stemming, stopword removal, and statistical feature selection, to study the impact of these tools on the classifiers’ performance in the literary setting. Existing studies outside the literary domain indicated that SVMs are generally better than naïve Bayes classifiers. However, in this study SVMs were not all winners. Both algorithms achieved high accuracy in sentimental chapter classification, but the naïve Bayes classifier outperformed the SVM classifier in erotic poem classification. Self-feature selection helped both algorithms improve their performance in both tasks. However, the two algorithms selected relevant features in different frequency ranges, and therefore captured different characteristics of the target classes. The evaluation results in this study also suggest that arbitrary feature-reduction steps such as stemming and stopword removal should be taken very carefully. Some stopwords were highly discriminative features for Dickinson's erotic poem classification. In sentimental chapter classification, stemming undermined subsequent feature selection by aggressively conflating and neutralizing discriminative features.

Legislative speech records from the 101st to 108th Congresses of the US Senate are analysed to study political ideologies. A widely-used text classification algorithm – Support Vector Machines (SVM) – allows the extraction of terms that are most indicative of conservative and liberal positions in legislative speeches and the prediction of senators’ ideological positions, with a 92 per cent level of accuracy. Feature analysis identifies the terms associated with conservative and liberal ideologies. The results demonstrate that cultural references appear more important than economic references in distinguishing conservative from liberal congressional speeches, calling into question the common economic interpretation of ideological differences in the US Congress.

Crowdsourcing Participatory Evaluation of Medical Pictograms Using Amazon Mechanical Turk