SKU: 52649492696

Opinion Mining for Software Development

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Description

Opinion Mining for Software DevelopmentOpinion mining, which uses computational methods to extract opinions and sentiments from natural language texts, can be applied to various software engineering (SE) tasks. For example, developers can mine user feedback from mobile app reviews to understand how to improve their products, and software team leaders can assess developers' mood and emotions by mining communication logs or commit messages. Also, the growing popularity of technical Q&A

Opinion mining, which uses computational methods to extract opinions and sentiments from natural language texts, can be applied to various software engineering (SE) tasks. For example, developers can mine user feedback from mobile app reviews to understand how to improve their products, and software team leaders can assess developers' mood and emotions by mining communication logs or commit messages. Also, the growing popularity of technical Q&A websites (e.g., Stack Overflow) and code-sharing platforms (e.g., GitHub) made available a plethora of information that can be mined to collect opinions of experienced developers (e.g., what they think about a specific software library). The latter can be used to assist software design decisions.

However, such a task is far from trivial due to three main reasons: First, the amount of information available online is huge; second, opinions are often embedded in unstructured data; and third, recent studies have indicated that opinion mining tools provide unreliable results when used out-of-the-box in the SE domain, since they are not designed to process SE datasets.

Despite all these challenges, we believe mining opinions from online resources enables developers to access peers' expertise with ease. The knowledge embedded in these opinions, once converted into actionable items, can facilitate software development activities.

We first investigated the feasibility of using state-of-the-art sentiment analysis tools to identify sentiment polarity in the software context. We also examined whether customizing a neural network model with SE data can improve its performance of sentiment polarity prediction. Based on the findings of these studies, we proposed a novel approach for recommending APIs with rationales by mining opinions from Q&A websites to support software design decisions. On the one hand, we shed light on the limitations researchers face when applying existing opinion mining techniques in SE context. On the other hand, we illustrate the promise of mining opinions from online resources to support software development activities.

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SKU: 52649492696

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Sophia Yeh
Los Angeles, US
★★★★★ 3
Affordable knives, horrible knife block
Size: 14 Pcs, Color: Red, Size: 14 Pcs, Color: Red
Knives are sharp and aren’t super rust resistant, but they get the job done. The knife block however, is horrible. It wasn’t sanded down or polished, so wood shavings can get into the grooves of the knife whenever you insert or take out the knives from the block.
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Reviewed in the United States on January 13, 2026
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THOMAS F COURTNEY
Los Angeles, US
★★★★★ 5
Moss & Stone Knives Sharpness
Size: 14 Pcs, Color: Black
Knives cut great. Nice and sharp.
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Reviewed in the United States on May 20, 2026
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Arlet
Houston, US
★★★★★ 5
Si quieres algo sencillo económico está es tu mejor opción
Size: 14 Pcs, Color: Black, Size: 14 Pcs, Color: Black
Necesito algo económico y básico para mi nuevo hogar y esta es una excelente opción
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Reviewed in the United States on June 2, 2026
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Clary Elizabeth Domínguez lebron
Bozeman, US
★★★★★ 5
Cuchillos
Size: 14 Pcs, Color: Black
10/10 igual que la imagen y la calidad súper buena
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Reviewed in the United States on June 4, 2026
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Carp_all_the _way
Cuba, US
★★★★★ 5
Well balanced sharp blades.
Excellent knife set. Versatile and very well made. Full tang, very well balanced blade. Sharp. Cuts better than many, much more expensive knives.
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Reviewed in the United States on June 4, 2026

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