Qi Gao, Fabian Abel, Geert-Jan Houben, Yong Yu 44.6% Impact of the access behavior on the type of concepts mentioned in the Organization o.7%0.6%09%/0.4%3.%/.9%3.3%/4.5%冒 12.4%/12.3%17.4%/4.9%8.1%/6.7%7.6%/8.7% Table 4. Semantic analysis overall and impact of Fig 3. Semantic analysis for indi- ccess behavior on the semantics vidual users individual Chinese and English microposts, we hypothesize that this is caused by the expressivity of the Chinese language: while Twitter users are often forced to leave out entities or use abbreviations to refer to entities. Sina Weibo users can exploit the 140 characters more effectively Table 4 illustrates how the access behavior infuences the semantics of th icroposts. When users publish posts from their mobile devices, then it becomes less likely, in comparison to access via desktop(tailored Web) applications, that a message mentions an entity. For microposts that are byproducts of other Web activities(e.g activities on Foursquare), we observe that it becomes more likely that entities and particularly location entities are mentioned in a post on Twit ter. In contrast on Sina Weibo users mention more entities in context of their standard microblogging activities Findings The results of the analysis illustrate the commonalities and differences regarding the semantic meaning of the microposts that users publish on Sina Weibo and Twitter respectively(see RQ5 and RQ6 in Sec. 3.1) F5: The topics that users discuss on Sina Weibo are to a large extent re- lated to locations and persons. In contrast to Twitter, users on Sina Weibo avoid talking about organizations such as political parties or other institu- tions. Overall, the semantics of Sina Weibo messages can be better extracted than the semantics of tweets. Consequently, when modeling the microblog ging activities for individual users, entity-based user profiles 4 can more successfully be generated for Sina Weibo users: for 92. 1% of them one can identify at least one entity of interest in comparison to 89.9% on Twitter F6: The type of applications via which users access the microblogging ser- vices, affects the occurrence of semantic concepts in the microposts. On mobile devices people tend to mention less entities than on desktop devices Furthermore, microposts on Twitter are more likely to mention entities and locations particularly if the post was generated as a byproduct of an activity 4.4 Sentiment Analysis Results The sentiment analysis provided by our framework classifies microblog posts as either positive, negative or neutral. Overall, 83. 4% and 82.4% of the Sina Weibo and Twitter posts respectively were classified as neutral. Table 5 he sentiment polarities of those posts th8 Qi Gao, Fabian Abel, Geert-Jan Houben, Yong Yu type of proportion of posts posts Weibo Twitter Location 58.4% 44.6% Organization 3.3% 16.0% Person 38.3% 39.4% Impact of the access behavior on the type of concepts mentioned in the posts Desktop/ Microblog/ Desktop/ Microblog/ Mobile Byproduct Mobile Byproduct Location 11.2%/6.6% 15.5%/4.0% 9.3%/8.4% 8.9%/13.7% Organization 0.7%/0.6% 0.9%/0.4% 3.5%/2.9% 3.3%/4.5% Person 12.4%/12.3% 17.4%/4.9% 8.1%/6.7% 7.6%/8.7% Table 4. Semantic analysis overall and impact of access behavior on the semantics. 0% 20% 40% 60% 80% 100% users 0 0.001 0.01 0.1 1 10 avg. number of entities per post Weibo Twitter Fig. 3. Semantic analysis for indi￾vidual users individual Chinese and English microposts, we hypothesize that this is caused by the expressivity of the Chinese language: while Twitter users are often forced to leave out entities or use abbreviations to refer to entities, Sina Weibo users can exploit the 140 characters more effectively. Table 4 illustrates how the access behavior influences the semantics of the microposts. When users publish posts from their mobile devices, then it becomes less likely, in comparison to access via desktop (tailored Web) applications, that a message mentions an entity. For microposts that are byproducts of other Web activities (e.g. activities on Foursquare), we observe that it becomes more likely that entities and particularly location entities are mentioned in a post on Twit￾ter. In contrast, on Sina Weibo users mention more entities in context of their standard microblogging activities. Findings The results of the analysis illustrate the commonalities and differences regarding the semantic meaning of the microposts that users publish on Sina Weibo and Twitter respectively (see RQ5 and RQ6 in Sec. 3.1): – F5: The topics that users discuss on Sina Weibo are to a large extent re￾lated to locations and persons. In contrast to Twitter, users on Sina Weibo avoid talking about organizations such as political parties or other institu￾tions. Overall, the semantics of Sina Weibo messages can be better extracted than the semantics of tweets. Consequently, when modeling the microblog￾ging activities for individual users, entity-based user profiles [4] can more successfully be generated for Sina Weibo users: for 92.1% of them one can identify at least one entity of interest in comparison to 89.9% on Twitter. – F6: The type of applications via which users access the microblogging ser￾vices, affects the occurrence of semantic concepts in the microposts. On mobile devices people tend to mention less entities than on desktop devices. Furthermore, microposts on Twitter are more likely to mention entities and locations particularly if the post was generated as a byproduct of an activity performed on another platform. 4.4 Sentiment Analysis Results The sentiment analysis provided by our framework classifies microblog posts as either positive, negative or neutral. Overall, 83.4% and 82.4% of the Sina Weibo and Twitter posts respectively were classified as neutral. Table 5 overviews the sentiment polarities of those posts that have been classified as