Introduction and Objective
Discuss about the Analysis of Social Media Comments and Product Service.
Marketers and companies make use of data of various types in order to promote and ultimately sell their products (Kwok 2013). Social media especially Facebook has become most attractive platform that giant company’s uses for arriving at marketing and promotional strategies related to their products. Facebook comments are used by multiple companies with data mining capabilities, who then conduct various types of analysis on them (Wang 2013). Statistical analysis of comments from Facebook data from various individuals can reflect trend regarding their personality, traits, likeliness and various other factors. The data generated can be used by marketers in making product selection relative to demography and then devise marketing strategies particular to target that segment (Dekay 2012).
The scope of this report has undertaken random sample of 300 comments inclusive of posts. Though these comments were taken from random samples yet it contained various trends related to individuals that can be used by marketers. These posts include nature, personality traits and ideas related to commenters that can be used for developing market segments (Bortree 2009). Then clusters were defined for groups of people, who had similar traits from their comment analysis. Individual’s distinctive attributes were analysed then products or services were recommended based on their psyche. The following are the objectives of the study;
- Study Objective 1:To understand individual’s Facebook comments on random topics
- Study Objective 2:To develop deeper understand of Facebook comments by identifying similar traits
- Study Objective 3:To analyse psyche and distinctive features of groups that have been clustered
- Study Objective 4:To understand products or service likeliness relative to cluster groups
Research was led for this investigation utilizing the system of substance examination. This investigation inspected the substance of more than 300 heterogeneous comments on Facebook pages and broke down what sorts of data are being presented by people. Baron Babbie (2009) in The Act of Social Exploration characterizes content examination as, “the investigation of recorded human interchanges, for example, books, sites, compositions and laws.” Content examination is an approach to efficiently watch the events of words, expressions, thoughts, or subjects in composed correspondences (Powell, 2004). To begin with, content examination empowers scientists to filter through huge volumes of information in a precise and orderly design Steve Stemler (2001).
Test Choice: For this investigation, the populace for inquire about was characterized as comments on Facebook pages from different sector of users. These comments were picked as the focal point of this investigation (Alarcón-del-Amo 2011). Keeping in mind the end goal to distinguish and group Facebook comments, a few distinctive hunt systems were connected. The main path was to check if the comments file from Facebook page gives a connection to its relating clusters. Albeit conceivably the data on Facebook pages is powerfully changing, data from an earlier day and age scarcely changes and consequently information covering that era can be considered as steady. Along these lines, the information was gathered which was data of Facebook posts amid the day and age. Information gathering comprised of filling helpful data into layouts, taking notes, and recording related screen captures of the Facebook page (Galvis Carreño 2013). The initial step was to lead an agenda as appeared in appendix (Table 2). Posts were sorted into classifications as indicated by their substance. In the wake of checking on the exploration tests, a few normal subjects or properties of divider posts was extricated and in this manner utilized for post arrangement in nine categories (table 2 in appendix). The information gathered about 309 posts was ranked in a scale of one to seven, where one indicated the lowest level and seven was the highest level of marking. Likewise, if a post incorporates both a remark and a “like” from a similar client, just the remark is checked, while remarks or “likes” are not tallied by any stretch of the imagination. All the posts were then rechecked for anomaly in statements and after agreement of the research team choice of cluster numbers was finalized (Gerolimos 2011). The analysis of variance for clusters revealed that all of the nine variables were statistically significant (p value of 0.000) in analysis the clustering.
Method
Despite the fact that this examination adequately developed a technique for estimating the utilization of Facebook pages by data clustering from various perspectives, constraints still exist (McCandless 2014). Analysing photographs and recordings might be a decent idea for, since they have a tendency to draw in more consideration and criticism as exhibited by the outcomes appeared in the tables and figures. For instance, a notice of a coming occasion can be observed with a review photo of this occasion rather than only content depictions so the post clustering would be clearer and conceivably pull in more consideration. When posting joins from different destinations, a brief and charming remark on the connection can be added to trigger the clients’ advantage and contribution. This technique for look into can’t record different sorts of record of web activity or age of the subjects or gender for that matter. The main information that was utilized is known as the “divider”, which is presumably the most mainstream Facebook highlight. The cluster analysis would be more appropriate and significant considering these fields of the subjects.
Data of random nature was collected from various samples of Facebook comments and processed to reveal trends related to person’s individual information. Field information was collected clarity of ideas, level of emotions, and level of objectivity, past perspective, now perspective, future perspective, level focus on personalities, level of criticism of corporate business, level of criticism of government or public services (Ahuja 2011). All these field data was collected using score method that was assigned for each comment collected. Score assigned to comments on the above categories ranged from 1 to 7, with 1 being the least score and 7 being highest possible score. When cluster centers were grouped according to logical assemblies which revealed various mean. It indicated that from random samples of 300 people cluster various trends and personality traits were reflected (Bonsón 2015). Applying Erikson’s stages of Psychosocial Development various psychological trends can be understood of persons belonging to specific cluster group. Homogeneity of categories were observed for cluster value k=3. The above given bar diagram reflects similarity of individuals when clusters were grouped in k=3.
According to Erikson’s stages of Psychosocial Development it needs to be ascertained age of the person’s whose comments have been analysed (McLeod 2013). It can be said when comments are analysed that age of individuals must be between 21 to 39 years of age or 40 years to 65 years of age. As per the given model psychosocial crisis experienced at 21 years to 39 years of age is related to intimacy or isolation, meaning young adults in their 30s. This group is generally concerned regarding finding the correct partner and is faced with fear of doing so, such that they do not have to face life alone (O Connor 2011). They need someone with whom they can interact and share phases of life with. At this stage some chooses to live their lives single, they tend to develop clarity of ideas. This group have high levels of emotions, they are concerned with their past perspectives as well as future perspectives. They are more bent on personality trends and are less critical regarding corporates or government (Sweetser 2008). This group has started earning and has high amounts of available disposable incomes; hence they are more prone to purchase expensive products as well as luxury items. They generally prefer fancy products; they desire to purchase products that would enhance their social status. Products that would make them happy or benefit them are those that would make them less dependent. A detailed analysis of their products and service preferences will be undertaken in the next section.
Limitation
In the second group that is analysed and categorised for the study, their characteristics features can be classified as generativity versus stagnation. At this stage adults are diagnosed, who feels more meaning with their work. At this stage, they feel they can contribute to the society or initiate a change process, and in case they fail they feel to be an unproductive member of the society. These people generally like value for money products and match their needs. During this stage most individuals have reached peak in their career, hence they are less willing to spend extra towards any show-off, they just maintain their social status to whichever segment they belong to (Wu 2014). This segment of people is generally critical regarding government or public services and corporate who are unable to provide a match for their products or services. They generally desire products or services that match their needs well and do not want to spend extra time in making purchase decision. This segment of the population is more difficult to be made happy as they are highly critical of products or services. They are merely associated with benefits arising from a particular product.
Another theory that can reflect regarding consumer behaviour is Icek Ajzen and Martin Fishbein. The theory focuses importance on existing attitudes of consumers towards decision-making process. Most important aspect underlying the theory is existing attitudes of consumers that make them provide or receive a particular type of behaviour. Analysing clusters according to theory reveals that;
Cluster 1 that has more consumers, who are focused on the future compared and have better objective with critical approach. Analysing decision making capabilities of this cluster group it can be said that they are more likely to be critical regarding various products, having choice that have connectedness to future utility.
Cluster 2 is less critical in nature and has an objective perspective. This segment of cluster has critical decision making capability with relatively less understanding related to utility of products or services.
Cluster 3 behaviour reflects less critical approach with perspective for future. They are less eager to criticize products and services hence retail products with relative less specification can be forwarded across to them.
Cluster 4 has again critical set of clients. Decision making characteristics for this group reveals greater clarity of ideas with a critical perspective with high emotions. Marketers can easily make use of such data analysis to offer their products accordingly in social media platform in form of advertisements or whatsoever. This model reflects that Big Data can convert into profits for companies, which can use these analysis tactics for targeting right customers.
Segments Developed
Engel, Kollet, Blackwell (EKB) Model is another model that expands scope of Theory of Reasoned Action. This model provides a five step process of consumer purchase decision, where consumer collects data and then processes information for comparing it with expectations. According to this theory the Clusters cannot be analysed in great detail. Motivation-Need theory is another critical theory in this domain apart from Hawkins Stern Impulse Buying. In Hawkins theory of impulse buying, Hawkins Stern highlighted the concept pertain to impulse behaviour in customers. His analysis was focused on rationale purchasing behaviour as against impulse fit. An impulse buying was he said, dependent upon external stimuli, which can help analyse cluster for this Facebook comments. But most accurate theory that can be applied to cluster is Theory of Reasoned Action.
Analysing comments relative to various segments of the clusters, products and services recommendations can be developed (Dashtipour 2016). The final cluster developed has four groups revealing various trends that can be analysed as given below;
Cluster 1: The name for this cluster can be “Critical Thinkers with Future Perspectives”. Analysing psychographic of this cluster segment is highly critical of government or public and corporate services. They have perspectives related to future and current state. They experiences or attaches less importance to emotions and are relatively less focused on personalities or ideas. This cluster is very active set of people, who aims at contributing to several issues ranging from government services to public companies, new perspectives, and future and so on. This cluster group has high ideas that they are eager to express in various forums. This group can belong to young generation who are employed across various service categories and have opinions regarding every matter. They will generally be selective and choosy regarding their product, meaning products with varied genuine attributes will attract them. Products or services that can be marketed to them are electrical vehicles, solar panels, latest mobiles and electronic appliances.
Cluster 2: In order to characterize this cluster an ideal name is “Contemporary Legacy”. This cluster has higher score on criticism related to business and attaches importance to past perspectives. Psychographic analysis of this group clearly reveals that they have less inclination towards future perspectives or towards clarity of ideas or emotions. The second cluster might be retired employees or aged having relatively less idea regarding what is taking place. They are relatively reluctant to new ideas or any process of change. They are less objective in nature; hence this cluster will be expected to be less critical regarding various products. Though this cluster has attachment to the past but has significant inclination towards new thinking patterns, products and other services. Products that can be marketed to this cluster is antique furniture, books, watches, travel and tourism at heritage places, vintage cars, boutique hotels, houses or apartments and appliances.
Cluster 3: This cluster can be called ideally, “Mediocre Purchasers” In this cluster, it can be characterized by people with having relatively similar reactions in varied aspects. This cluster is relatively less involved in expressing their views or opinion in social media forum. Moreover, trends regarding this cluster depict that they might belong to family individuals with greater emotional levels. This segment will be more attached to product that comes with sentimental value. This segment does not have any predisposition towards any particular thought processes. Psychographic factor related to this group reveal that almost any type of products or services can be marketed to them as grocery, clothing, mobiles, appliances, hotels, home stays, books and so on.
Cluster 4: This group can be referred to as “Critical Thinkers with Objective”. In this category cluster, there is significant level of importance attached to criticism of corporate or businesses, past perspective and objectivity. This cluster of individuals is balanced criticizers with perspectives related to the future as well as past. Analysis of psychographic variable for this cluster enables us to offer products related to grocery, clothing, furniture and apparels, automobiles and so on.
Reference Lists
Ahuja, Vandana, and YajuluMedury.”sCorporate blogs as tools for consumer segmentation-using cluster analysis for consumer profiling.” Journal of Targeting, Measurement and Analysis for Marketing 19, no. 3-4 (2011): 173-182.
Alarcón-del-Amo, María-del-Carmen, Carlota Lorenzo-Romero, and Miguel-Ángel Gómez-Borja. “Classifying and profiling social networking site users: A latent segmentation approach.” Cyberpsychology, behavior, and social networking14, no. 9 (2011): 547-553.
Bortree, Denise Sevick, and Trent Seltzer. “Dialogic strategies and outcomes: An analysis of environmental advocacy groups’ Facebook profiles.” Public Relations Review 35, no. 3 (2009): 317-319.
Bonsón, Enrique, Sonia Royo, and Melinda Ratkai.”Citizens’ engagement on local governments’ Facebook sites. An empirical analysis: The impact of different media and content types in Western Europe.” Government Information Quarterly32, no. 1 (2015): 52-62.
Dashtipour, Kia, SoujanyaPoria, Amir Hussain, Erik Cambria, Ahmad YA Hawalah, Alexander Gelbukh, and Qiang Zhou. “Multilingual sentiment analysis: state of the art and independent comparison of techniques.” Cognitive computation 8, no. 4 (2016): 757-771.
Dekay, Sam H. “How large companies react to negative Facebook comments.” Corporate Communications: An International Journal 17, no. 3 (2012): 289-2.
GalvisCarreño, Laura V., and Kristina Winbladh. “Analysis of user comments: an approach for software requirements evolution.” In Proceedings of the 2013 International Conference on Software Engineering, pp. 582-591. IEEE Press, 2013.
Gerolimos, Michalis. “Academic libraries on Facebook: An analysis of users’ comments.” D-Lib Magazine 17, no. 11 (2011): 4.99.
Kwok, Linchi, and Bei Yu. “Spreading social media messages on Facebook: An analysis of restaurant business-to-consumer communications.” Cornell Hospitality Quarterly 54, no. 1 (2013): 84-94.
McCandless, Timothy P., and MehrshadSetayesh.”Method and system for performing trend analysis of themes in social data.” U.S. Patent Application 13/795,802, filed September 18, 2014.
McLeod, Saul. “Erik Erikson.” Retrieved August 9 (2013): 2013.
O Connor, Peter. “An analysis of the use of Facebook by international hotel chains.” (2011).
Sweetser, Kaye D., and Ruthann Weaver Lariscy. “Candidates make good friends: An analysis of candidates’ uses of Facebook.” International Journal of Strategic Communication2, no. 3 (2008): 175-198.
Wang, Yi-Chia, Moira Burke, and Robert E. Kraut. “Gender, topic, and audience response: an analysis of user-generated content on facebook.” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 31-34.ACM, 2013.
Wu, Hsin-Ying, Kuan-Liang Liu, and Charles Trappey. “Understanding customers using Facebook Pages: data mining users feedback using text analysis.” In Computer Supported Cooperative Work in Design (CSCWD), Proceedings of the 2014 IEEE 18th International Conference on, pp. 346-350. IEEE, 2014.
Order an Essay Now & Get These Features For Free:
Turnitin Report
Formatting
Title Page
Citation
Outline
