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TMTA: Motives for Immigrating

Page history last edited by julissa01@umail.ucsb.edu 7 years, 5 months ago

TMTA: Motives for Immigrating


Group Members: Evelyn Ramirez-Mancilla and Julissa Villlatoro

Group Annotated Bibliographies: Evelyn Ramirez-Mancilla and Julissa Villatoro

Group Research Reports:  Evelyn Ramirez-Mancilla and Julissa Villatoro




The objective of this project is to explore and identify any dominant "topics"  in Hispanic/Latino Literature that reflect the motives or circumstances that make people immigrate from their native country.  Further, the team wanted to study the practice of topic modeling as an effective analytical tool for testimonials such as the works used for this research project.   



Project Description:

This project analyzes the  catalyst for immigration to the United States in the nonfiction text  Amigas: Letters of Friendship and Exile (an account of the narrator’s experiences in Chile), December Sky: Beyond my Undocumented Life (an account of the narrator’s experiences in El Salvador), and I Rigoberta Menchu: an Indian Woman in Guatemala (an account of the narrator’s experiences in Guatemala). From each text, the project team selected a passage that each felt best described the social and cultural climate that may have forced each narrator to flee their respective countries.  Further, the team selected these particular works based on how familiar each member was with the work.  One piece where one member is familiar with the work, another where the other is familiar with the work, and one more where both members were familiar with the work.  Afterward, each member generated topic models for the other to decipher and interpret.




Topic Modeling Tool, is a graphical interface tool that allows the group to create  “topics” or “discourses” as Graham, Weingart and Milligan have described in their article. The project group, placed the passages that recount the catalyst that influenced each character to leave the country into the Topic Modeling Tool.  Running through several trials of inputting the text into the system, the team experimented how many topics we wanted the program to analyze.  Experimenting with these settings, we identified a couple of topics that appeared prevalent through each test run.  The team attempted to categorize these topics into four lists containing words that appeared to have a strong association with each other.  This was narrowed down to military/politics, economics, violence and other sentimentals and/or emotions generated during their time there.  Each topic was then distinguished by color:  Political/Military (Red),  Economic (Green), Violence (Orange), and Semantics(Yellow) We have come to believe that these topics we uncovered further supported an overarching discourse within these three separate accounts: a violent conflict erupted within countries in Latin America that ultimately forced these young women to flee.  




The team focused efforts on attempting to understand if the topic modeling tool could identify the main motifs and themes, specifically if we can track what kinds of circumstances prompted the women in their respective testimonials to leave their country.  Additionally, each team member interpreted each sets of topic model differently, we found that the different perspectives created a new discourse about each text.  For example, one team member had not read the book, “Rigoberta”, and simply read the topic model that the other group member generated for her to interpret on her own.  She came to the conclusion that Rigoberta, the Indigenous woman in the "Rigoberta Menchu" text, was an orphan from Nicaragua, and had to flee the country for more economic reasons.  This gave a new perspective to the other member who was familiar with the text, because while she understood the violence that forced the narrator to flee, the economic situation was also an important issue that she needed to overcome as she battled to survive in her own native home.   Despite the slight misinformation, specifically because Rigoberta is actually from Guatemala, and not Nicaragua.  However, the passage that the computer analyzed contained the country Nicaragua, and had categorized Nicaragua as an important topic, which explains this misinformation.   



Closing Thoughts:

While it does prove difficult to come to solid conclusions about making sense of the patterns through sight, this experiment did show the team that by using topic modeling, the project team found that despite having little to no knowledge about a work, meaningful interpretations and data can still be found.  For this project, the team came to the conclusion that the women in these accounts did not simply immigrate from their homes-- they fled from poverty and death.  In conclusion, combined with a close reading analysis, and perhaps finding a better tool that properly analyze these results better, topic modeling proves to be an interesting asset to discover different ways a work of nonfiction.  



Graham, Shawn, Scott Weingart, and Ian Milligan. "Getting Started with Topic Modeling and

     MALLET." Web log post. The Programming Historian. N.p., 2 Sept. 2012. Web. 7 Dec. 2014.


"Topic Modeling Tool." Topic-modeling-tool - A Graphical User Interface Tool for Topic Modeling.

     N.p., n.d. Web. 07 Dec. 2014.


Posner, Miriam, Andy Wallace, and Zoe Brovosky. "Very Basic Strategies for Interpreting Results from the Topic Modeling Tool." Web log post. Miriam Posner's Blog. Word Press, 29 Oct. 2012. Web.<http://miriamposner.com/blog/very-basic-strategies-for-interpreting-results-from-the-topic-modeling-tool/














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