Spring 2016
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This document outlines the objectives and requirements for a research project being conducted by the UOEC club. Small teams will choose a topic to research, gather quantitative data, use STATA to run regressions, and answer a provided question on how independent variables affect the dependent variable. Teams are then free to further analyze the data. Each team must have an executive member experienced with econometrics. Teams will present their findings to the club. The document provides a list of objectives for participants and resources for gathering data.
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bibliometric review of 523 research articles published in the Scopus database (between 2018 and 2022) to
discern the content and thematic analysis. Findings suggested that key purposes of the sentiment analysis
are mainly related to innovation, transparency, and efficiency. Our review also highlights the
distinctiveness of sentiment analysis for synthesising social media information to investigate various
features, including the knowledge-domain map that detects author collaboration networks in the past.
Sentiment Analysis for Software EngineeringHow Far Can We G.docx
Sentiment Analysis for Software Engineering:
How Far Can We Go?
Bin Lin
Software Institute
Università della Svizzera italiana (USI)
Switzerland
Fiorella Zampetti
Department of Engineering
University of Sannio
Italy
Gabriele Bavota
Software Institute
Università della Svizzera italiana (USI)
Switzerland
Massimiliano Di Penta
Department of Engineering
University of Sannio
Italy
Michele Lanza
Software Institute
Università della Svizzera italiana (USI)
Switzerland
Rocco Oliveto
STAKE Lab
University of Molise
Italy
ABSTRACT
Sentiment analysis has been applied to various software engineer-
ing (SE) tasks, such as evaluating app reviews or analyzing develop-
ers’ emotions in commit messages. Studies indicate that sentiment
analysis tools provide unreliable results when used out-of-the-box,
since they are not designed to process SE datasets. The silver bul-
let for a successful application of sentiment analysis tools to SE
datasets might be their customization to the specific usage context.
We describe our experience in building a software library recom-
mender exploiting developers’ opinions mined from Stack Overflow.
To reach our goal, we retrained—on a set of 40k manually labeled
sentences/words extracted from Stack Overflow—a state-of-the-art
sentiment analysis tool exploiting deep learning. Despite such an
effort- and time-consuming training process, the results were nega-
tive. We changed our focus and performed a thorough investigation
of the accuracy of commonly used tools to identify the sentiment of
SE related texts. Meanwhile, we also studied the impact of different
datasets on tool performance. Our results should warn the research
community about the strong limitations of current sentiment anal-
ysis tools.
CCS CONCEPTS
• Information systems → Sentiment analysis;
KEYWORDS
sentiment analysis, software engineering, NLP
ACM Reference Format:
Bin Lin, Fiorella Zampetti, Gabriele Bavota, Massimiliano Di Penta, Michele
Lanza, and Rocco Oliveto. 2018. Sentiment Analysis for Software Engineer-
ing: How Far Can We Go?. In ICSE ’18: ICSE ’18: 40th International Conference
on Software Engineering , May 27-June 3, 2018, Gothenburg, Sweden. ACM,
New York, NY, USA, 11 pages. https://doi.org/10.1145/3180155.3180195
Permission to make digital or hard copies of all or part of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full citation
on the first page. Copyrights for components of this work owned by others than the
author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or
republish, to post on servers or to redistribute to lists, requires prior specific permission
and/or a fee. Request permissions from [email protected]
ICSE ’18, May 27-June 3, 2018, Gothenburg, Sweden
© 2018 Copyright held by the owner/author(s). Publication rights licen.
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Spring 2016
- 1. UOEC 1 Spring 2016 Jean Ramirez Chair of Research STATA Research Project For this term’s research, participants will be grouped in small teams to gather quantitative data from their topic of their choice, use the program STATA to run regressions, and answer a provided question. The provided question will be the baseline of the project, but each team is free to use STATA to further analyze the gathered quantitative data. Each team will be given time to present their project to the club. Objective: To utilize research skills Work with quantitative data Learn how to use “general-purpose statistical software” like STATA - Wikipedia Analyze quantitative data Utilized Econometrics Skills Critical Thinking Team Work Communication and presentation Skills Each team has to answer: How does the independent variable(s) affect in dependent variable? After answering this question, each team is free to analyze the data even further. This means implement learned material from Econometrics. After analyzing the data it is encouraged to create a story of how do the independent and dependent variables interact and how does that translate in the real world. It is also encouraged to provide some graphs and qualitative data to enhance the analytical work for a presentation. Each team will have an Executive member who has take the Econometrics sequence classes. Basic resources are provided on the song page.
- 2. UOEC 2 Resources: https://research.stlouisfed.org/fred2/ http://data.worldbank.org GDP- http://data.worldbank.org/indicator/NY.GDP.MKTP.KD Population - http://data.worldbank.org/indicator/SP.POP.TOTL http://www.imf.org/en/data http://www.who.int/gho/database/en/ https://www.fbi.gov/stats-services/crimestats Oil production - https://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=50&pid=53&aid=1 Sports: http://community.amstat.org/sis/sportsdataresources Also we have clean Data on: Minimum wage and traffic fatalities Oregon Healthcare system Upward mobility