Data analysis, results, and discussions from example study undertaken

Social media methodology framework analysis and get the data of place location, time, rating, reviews from Google Maps through Stevesie and Octopus successfully. These data are useful for further individual research and diverse enough to cover different groups of people. Moreover, from these original data, we can analysis the sentiment and emotion of the sentences and keywords.

Post processing of text looking for keywords, etc.

During the process of post processing of text looking for keywords, we get these sentences for example: ‘‘enjoy the weather and get fresh air’’; ‘‘follow the seasons, flora and fauna’’; ‘‘reduce stress, relax’’; ‘‘exercise, keep in shape’’; ‘‘do something together with friends/family’’; ‘‘obtain peace and quiet without noise’’; ‘‘other reasons and never get to green space’’. As well as the keywords below for example: Fresh, air, breeze, cool, sun; Flora, fauna; Relax; Exercise, running, jogging, work out, walk; Stress, calm; Family, friends; Peace, quiet, noise; Green, greenery, trees, plants.

2.29 Conclusions, Limitations, and Further Studies

Before starting the research, pre-processing of text should be carefully considered as well, for instance with text normalization, getting rid of stop words, or repetitive words, etc. – the system might clean the text in a way that important words get omitted which would otherwise influence the analysis result (e.g., sentiment of aspect-qualifier categorization).

The analysis template developed by MonkeyLearn which is used for picking out aspect-qualifier-action word categories is useful in revealing values, motivations, and activities that take place. They are typically subject to a grouping system by the software and may result in different associations as one paragraph can be extracted into several combinations of word grouping. It is then up to the researcher to pick up groupings that are meaningful for the purpose of the research question. It seems extremely complicated to develop a system which would take over both pre-processing and post-processing of textual data while carefully considering each word or phrase, and their meaning in the context of a whole statement. For now, it is inevitable to run through the textual data analysis process manually in order to ensure textual meaning is properly considered in post-processing outcomes. However, if these aspects are understood, pre-processing and post-processing of text can be more comprehensively achieved with the help of different software, in order to process the textual data for a given purpose. In this respect, when formulating research questions, it is equally important to understand the limitations and possibilities of automated textual processing within the research scope.

Generally speaking, the different ways of using social media data explored in each individual process highlight the fact that this type of analysis needs to be conducted along with other research methods, such as network analysis, population character, building typology, and more.

A major challenge of text based social media analysis is related to human versus machine (software) reading analysis. Complications occur throughout the whole process, from the very beginning. For instance, some reviews obtained from Google Maps, were directly translated by Google, mainly from Cantonese or Mandarin into English. The extracted reviews were then downloaded as a batch in English translation. We therefore presume that there might be some contextual bias as a result of translation (use of wording, phrasing, etc. – may influence core meaning or sentiment of review as these lingual elements are unique in each language and may bare meaningful expressions).

Therefore, for future studies we should focus on these aspects:

• More intelligent NLP (Natural Language Processing) software is needed for researchers to use. In manually filtering Google Maps comments, we will find many problems, and each problem needs a separate script to identify. This also happened on Weibo, a widely used social platform in China Because many people can make profits by sending irrelevant information, the automatic shielding system often fails to shield some hidden irrelevant information or shield relevant information. Malicious users are an important issue in social media.

• A more accurate or widely accepted way to quantify social media data is needed. Because most of the research data are based on mathematics, there has to be such a transformation process for social media data to find ways to interact with other types of data.

• Standards for social media data need to be established. This standard does not need to be particularly strict, because there is a large gap between users of different social media themes. However, if standards are not set, the research on a single social media cannot be applied to other social media. Cross social media research is needed to find the operation logic of different social media, so as to formulate a wide range of standards applicable to different social media.

List of References

• Ilieva, R.T. and McPhearson, T. (2018) “Social-media data for urban sustainability,” Nature Sustainability. Nature Publishing Group, pp. 553–565. doi:10.1038/s41893-018-0153-6.

• Martí, P., Serrano-Estrada, L. and Nolasco-Cirugeda, A. (2019) “Social Media data: Challenges, opportunities and limitations in urban studies,” Computers, Environment and Urban Systems, 74, pp. 161–174. doi:10.1016/j.compenvurbsys.2018.11.001.

• Mayr, P. and Weller, K. (2017) “Think Before You Collect: Setting Up a Data Collection Approach for Social Media Studies,” The SAGE Handbook of Social Media Research Methods, pp. 107–124. doi:10.4135/9781473983847.N8.

• CRAWL, B. 2022. Botsol Crawl [Online]. Available: https://www.botsol.com/ [Accessed].

• SOCIALMENTION. 2022. Socialmention [Online]. Available: http://www.socialmention.com/ [Accessed].

• STEVESIE. 2022. stevesie [Online]. Available: https://stevesie.com/cloud/apis [Accessed].

• VICINITAS. 2022. vicinitas [Online]. Available: https://www.vicinitas.io/ [Accessed].