Understanding what motivates a gamer to pay to play a game could enhance the gaming industry. One of the ways to understand game players experiences is through exploration via their text communication. This study explores text communication posted on the Steam digital distribution platform on paid-play games. A total of 7255 gamer text communicated were collected and analysed. Reviews from the past pinpointed that elements such as social influence, entertaining features, graphic design, music, and soundtrack used in the game could influence the game playing experience. To study those, we use mix-method (qualitative and quantitative) methods to analyse the data. We invite three human coders to categorise statements associating with its elements and aspects. Next, with the aid of Wmatrix, a semantic analysis system, we tabulate and elaborate the emotion words associating with the gameplay elements. The result shows that the positive feeling of love and joy highly encourages the gamer's overall game playing experience. Elements such as the storyline, design of the Avatar and the entertaining features are the primary motivation of the gameplay.

Airbnb is a peer-to-peer accommodation website in the sharing economy. Past studies have examined the factors associated with Airbnb utilization from various platforms, but not exclusively from Twitter. A total of 21,097 tweets was collected in a period of two months, and the tweets were qualitatively analyzed with the help of text analysis tools to verify the discourse of discussion. Literature was reviewed for common factors attracting clients to an Airbnb accommodation. Factors were then qualitatively analyzed and compiled using Wmatrix, and the themes that emerged were: Price and status, social interaction and communication, location, reputation, amenities and a pet-friendly environment. This result provides a deeper insight to Airbnb hosts to strategize and add value to their current market situations.

Emoticons are pictographic representations of facial expressions that are used to convey emotions in text messages and other similar methods of communication. Most research on emoticons has examined how they are used in public forums rather than in private messaging. Using a sample of undergraduate students (n=106; male 52.83%; mean age 20.26 years, SD 1.93), this study examines the use of emoticons in private text communication. Results reveal that emoticon usage is highest amongst friends, followed by siblings, then parents, other family members and more distant connections. Emoticons representing positive emotions are more commonly used than those representing strong negative emotions. Emoticons representing relief were found to be used particularly within peer group communication, whereas emoticons representing contentment were used more with family members and other, more distant, connections. The use of the "Relieved" emoticon with peers may reflect overcoming the stressors associated with shared educational challenges, whereas using the "Content" emoticon outside peers and family may represent emotional modulation and presentation.

There are various factors that affect the sentiment level expressed in textual comments. Capitalization of letters tends to mark something for attention and repeating of letters tends to strengthen the emotion. Emoticons are used to help visualize facial expressions which can affect understanding of text. In this paper, we show the effect of the number of exclamation marks used, via testing with twelve online sentiment tools. We present opinions gathered from 500 respondents towards "like" and "dislike" values, with a varying number of exclamation marks. Results show that only 20% of the online sentiment tools tested considered the number of exclamation marks in their returned scores. However, results from our human raters show that the more exclamation marks used for positive comments, the more they have higher "like" values than the same comments with fewer exclamations marks. Similarly, adding more exclamation marks for negative comments, results in a higher "dislike".

RSS (Really Simple Syndication), an XML-based format for sharing and distributing web content to the RSS reader has actively been used for retrieving the latest update's weblogs information among the bloggers in blogosphere environment. However, RSS aggregators that are used to feed the RSS web-contained from the different formats have lead to the issues of RSS syndication confusion. Although their ability to aggregate the latest updated weblogs in timely manner, weblogs that are published in chronological order from multiple sites with different RSS syndication format have lead to ambiguity problem, resulting an uncategorized and irrelevancy of aggregated result. In this paper, a synthetic analyzer named PheRSS is presented. PheRSS is able to collect the different syndication format to achieve a broader set of metadata. With the rules and constraints applied, a more relevant set of data based on user interest can be obtained. The experiment is done to evaluate the accuracy of the result. It is founded that the result achieved better accuracy comparing to Feed Demon.