AI and Culture: Machine Learning in Practice Assignment Sample

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Introduction

Artificial Intelligence (AI) refers to the set of diverse technologies which support multiple advanced functions. AI has transformed various fields and enables analysts to engage with cultural datasets in diverse ways (Mustak et al., 2021). Topic Modelling is one of the prominent methods within AI; it is a statistical technique that uses machine learning to identify clusters or groups of similar words within a body of text, making it especially relevant for students seeking assignment writing help in understanding complex analytical methods. Topic Modelling has gained significant popularity in information retrieval and computational linguistics. The current essay examines how Topic Modelling contributes to metadata enhancement and thematic exploration, enabling more effective identification of topics and themes within a corpus.

Clustering of words tends to occur in multiple documents and the motive is to identify the group of words. Topic Modelling is just not limited to identify hidden text, it often provides richer context for understanding cultural currents that have been reflected in literature (Mustak et al, 2021). The investigation would be based on a specific research question "How Topic Modelling can enhance interpretative potentials of a corpus of novels?" The in-depth discussion would be undertaken while mentioning strengths and weaknesses associates with Topic Modelling. Furthermore, significant emphasis will apply on risk of oversimplification within complex narratives in Topic Modelling, which is a key concern explored in AI and Culture: Machine Learning in Practice.

AI and Culture: Machine Learning in Practice Assignment Sample

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Main Body

Topic Modelling depends upon Natural Language Processing [NLP] and subsequently, analyses textual data. For preparing corpus of novel while using this technique varied steps are needed to be performed (Zhao et al, 2021). It starts with ensuring that text data is cleaned and any kind of error or noise which can misrepresent the analysis requires to be removed. This comprised with eradicating numbers, irrelevant formatting and punctuation. Furthermore, lemmatisation and stemming proves to be important process as it supports in the reduction of words from base. This results in standardised of text and further permits model for identifying variation in the words. Hence, this is type of text mining method which is used of understanding unstructured data and creates a conclusive statement from data. The next step is to stop-word removal process, stop words commonly includes “is”, “the”, “on” these words does not provide meaningful information. Therefore, this needs to be removed from the text, after cleaning phase, data could be vectorised by using certain techniques such as Term Frequency-Inverse Document Frequency [TF-IDF] (Zhao et al, 2021).

After processing of dataset, Topic Modelling algorithms; Latent Dirichlet Allocation (LDA) recognises the cluster of text which develops in corpus. In this manner, LDA uncovers hidden components and this support scholar to undertake in-depth analysis and subsequently, effectual understanding can be developed (Aziz et al, 2022). LDA proves to be most popular topic modelling method, it focuses on uncovering hidden structures within set of observation by looking upon the words. It focuses on treating same topic in different distributions. The LDA model often breaks down topic within words and assumes that varied topics might have common words. (Aziz et al, 2022). For example, biology document contains the words like anatomy and reproduction while chemistry document comprised with the words such as chemicals, alloys and solutions. The model has varied mechanics and algorithm features, they reduce words and groups similar words pattern and develops topic cluster.

The foremost strength of Topic modeming comprehends with its ability of handling larger amount of theoretical information. The underlying themes does not immediately apparent through traditional literary analysis (Aziz et al, 2022). This method focuses on facilitating interdisciplinary research by integrating insights from literature, cultural and sociology studies. Furthermore, topic modelling supports in delivering quantitative information into qualitative insights. Analyst can effectively measure the prevalence of significant topics in a fix period of time and this reveal shift within cultural narratives (Aziz et al, 2022). For example; analysis of a novel based on the Great Depression era supports in identifying the societal concerns and economic differences evolved within literature. This results in developing empirical data that discourse around literary works and transform qualitative assessment to quantifiable evidences.

Topic Modelling supports in understanding the themes and main subject area within the text, it plays pivotal role in this context by going beyond individual words and further provide higher-level understanding related to main topics discussed within text corpus. It supports in understanding the themes in effectual manner and therefore, it is essential specially when the data is available in large quantity sets (Churchill and Singh, 2022). Further, topic modelling supports in document clustering and groups the similar document. It makes certain tasks easy such as document classification, content recommendation and information retrieval. By developing significant understanding related to topic users can personalise the document recommendations. It supports in the identification of certain documents which are linked with the topic preference. Hence, it is useful specially in the application of personalised news, targeted advertising and content filtering.

Whilst multiple strengths; Topic modelling has several limitations; the short text proves to be challenging for task and detection and extraction lacks in contextual information and this results in creating complexities related to data sparsity. Further, the inherent challenges of literature do not categorise it into the thematic labels (Sestino and De Mauro, 2022). This can result in misleading of topic and further provides wrong information to users. Hence, there is need to focus on careful consideration while undertaking interpretation by human analyst (Sestino and De Mauro, 2022). Users must be aware about model limitations, and this should be clear that Topic Modelling serves as an initial base for analysis instead of providing definite conclusion regarding specific context which aligns with discussions in AI and Culture: Machine Learning in Practice.

The algorithms are required to be implemented by human which can be measured appropriately, it might often concern with biasness which reflects about creators’ perspectives. In case, a set of data is primarily associated with specific demographics [black communities] then, topic will eventually describe about inclusion and diversity within literature. The cultural context under which theme has been written it also influences topic identification and highlights the significance of contextual awareness during interpretation of results (Colavizza et al, 2021). Findings have shown that currently, quality of the topic model relies on manipulating and refining data and this has been proven time-consuming in model domain. Human interaction with topic models results in biasness which makes it a subjective topic (Colavizza et al, 2021). The human interaction in the loop topic modelling developing the form of mixed initiative interaction so that system and user can work together with the aim of optimising model. Thus, these are the prior limitation and it is essential that users must be aware about these loopholes while using topic modelling.

The academic discourse is increasingly intersecting with data driven technologies; topic modelling is presenting a form of reductionism and focusing on the requirement of driving insights for undertaking own critical analysis. The interplay of qualitative findings in qualitative insights is essential as thematic insights developed via Topic Modelling which enhances the understanding of students to greater extent. It provides wider understanding related to socio-cultural context, further, it does not overshadow the quality of literary work. An effectual investigation can be undertaken that considers the different character for developing in-depth understanding. A comprehensive understanding related to themes which are operating in cultural and historical frameworks can be developed. Combining topic modelling techniques with the sentiment analysis helps in discovering about different aspects and features related to main context (Huang et al, 2023). This aspect can be further used in identifying the positive, negative and neutral experiences. Topic Modelling supports in organisation of data in useful manner and further develops significant insights which is crucial for understanding the topic. This works effectively with the larger data set and therefore, this should be implied with wider data sets so that challenges related to biasness can be reduced.

Hence, there is no doubt in articulating that Topic Modelling is a prominent element within AI that fosters understanding related to specific context (Mariani et al, 2022). It further transforms numerical instances into theoretical instances which makes it significant and effectual for developing in-depth understanding regarding topic context. However, there is need to focus on reducing human biasness as this can impact the efficacy of information.

Conclusion

Conclusively; this can be said that Topic Modelling has been providing significant opportunities for improving analysis within corpus of novel. It focuses on revealing hidden themes and pattern under the literary text and provides meaningful information. Thus, it supports users for identifying salient topic and undertaking their analysis, this results in providing detailed analysis of interdisciplinary exploration related to cultural data. However, limitation associates with algorithm biasness and short textual data signifying about the importance of focusing on critical approach that can supplement qualitative data with qualitative information. Hence, there is no doubt in stating that Topic Modelling serving as a powerful tool for the literary analysis. It is important to focus on the limitation so the quality of literature does not het compromised and scholar can effectively use information. This supports in developing nuanced understanding regarding literature based on cultural and historical context.

References

Books and Journals

• Aziz, S., Dowling, M., Hammami, H. and Piepenbrink, A., 2022. Machine learning in finance: A topic modeling approach. European Financial Management, 28(3), pp.744-770.
• Churchill, R. and Singh, L., 2022. The evolution of topic modeling. ACM Computing Surveys, 54(10s), pp.1-35.
• Colavizza, G., Blanke, T., Jeurgens, C. and Noordegraaf, J., 2021. Archives and AI: An overview of current debates and future perspectives. ACM Journal on Computing and Cultural Heritage (JOCCH), 15(1), pp.1-15.
• Huang, X., Zou, D., Cheng, G., Chen, X. and Xie, H., 2023. Trends, research issues and applications of artificial intelligence in language education. Educational Technology & Society, 26(1), pp.112-131.
• Mariani, M.M., Perez‐Vega, R. and Wirtz, J., 2022. AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing, 39(4), pp.755-776.
• Mustak, M., Salminen, J., Plé, L. and Wirtz, J., 2021. Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda. Journal of Business Research, 124, pp.389-404.
• Sestino, A. and De Mauro, A., 2022. Leveraging artificial intelligence in business: Implications, applications and methods. Technology Analysis & Strategic Management, 34(1), pp.16-29.
• Zhao, H., Phung, D., Huynh, V., Jin, Y., Du, L. and Buntine, W., 2021. Topic modelling meets deep neural networks: A survey. arXiv preprint arXiv:2103.00498.