Commit 569e6fcd authored by  Joel  Oksanen's avatar Joel Oksanen

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\chapter{Evaluation Plan}
Our extensions to ADA involve both quantitative and qualitative aspects, such as the accuracy of our sentiment analysis methods and the usability of our interface implementations, respectively. The evaluation is expected to take place in early June, and the plans for quantitative and qualitative assessment are detailed in the following sections.
\section{Quantitative assessment}
We will evaluate our sentiment analysis implementation both individually and as part of the entire pipeline of ADA.
The two sentiment analysis implementations can be evaluated on their own using hand labelled data sets, such as a freely available one from \cite{RefWorks:doc:5e2e107ce4b0bc4691206e2e} for target-dependent Twitter sentiment classification. These results can be compared to each other, and with baseline results in existing target-dependent sentiment analysis papers, discussed in section \ref{sec:sa}. Alternatively, w could label our own dataset of Amazon reviews, with which we could also test the feature extraction. In that case, we face the challenge of what constitutes a feature of a product.
We also evaluate our sentiment analysis and feature extraction implementations as part of ADA. This is possible by comparing the dialectical strength measure for a product to the product's aggregated user rating by calculating their Pearson correlation coefficient (PCC). The intuition is that a closer correlation between these two figures implies a better accuracy of the agent's semantic understanding. PCC scores for particular Amazon product domains can be compared with the PCC score for a wide domain of products, in order to determine the generality of our method. We can evaluate the performance of the ADA extensions by comparing our PCC score to the scores achieved in \cite{RefWorks:doc:5e08939de4b0912a82c3d46c} for Rotten Tomatoes: a somewhat similar score would constitute success as this domain is more challenging. Furthermore, we can possibly evaluate our ADA on the same Rotten Tomatoes review dataset, in order to further test how well it generalises to different settings.
The results from the individual evaluation of the feature-dependent sentiment analysis provide information on how well the ADA can distinguish between the different aspects of a product. This contrasts with the overall evaluation of the ADA, which only tells us if it understands the general sentiment towards a product. Quantitative evaluation of the ADA's feature-level understanding was not performed in \cite{RefWorks:doc:5e08939de4b0912a82c3d46c}, so it will be interesting to evaluate.
\section{Qualitative assessment}
We will evaluate the performance of our user interface implementations qualitatively via user feedback, guided by the RRIMS properties defined in section \ref{sec:rrims}. Having two implementations to compare will be fruitful in terms of determining their strengths and weaknesses.
\chapter{Introduction}
In this chapter, we will first discuss the motivations behind the project and then specify its main objectives.
\section{Motivations}
People spend an ever growing share of their earnings online, from purchasing daily necessities on e-commerce sites such as Amazon\footnote{https://www.amazon.com/} to streaming movies on services such as Netflix\footnote{https://www.netflix.com/}. As the market shifts online, people's purchase decisions are increasingly based on product reviews either accompanying the products on their e-commerce sites, or on specialised review websites, such as Rotten Tomatoes\footnote{https://www.rottentomatoes.com/} for movies. These reviews can be written by fellow consumers who have purchased the product or by professional critics, such as in the latter example, but what unites most online review platforms is the massive number of individual reviews: a particular type of electronic toothbrush can have more than 10,000 reviews on Amazon\footnote{https://www.amazon.com/Philips-Sonicare-Electric-Rechargeable-Toothbrush/dp/B00QZ67ODE/}. As people cannot possibly go through all of the individual reviews, purchase decisions are often based on various kinds of review aggregations. The presentation of a review aggregation must be concise and intuitive in order to be effective, but a good review aggregation will also retain some nuances of the original reviews, so that consumers can understand \textit{why} a product is considered good or bad, and if the reviewers' arguments align with their individual preferences.
Perhaps the most well-known review aggregation method is a product's average star rating out of five stars. Although this metric is simple to both implement and understand, it completely ignores the information in the accompanying review texts. To illustrate, consider this three-star Amazon review for the aforementioned toothbrush:
\begin{center}
\textit{The product is great but the packaging literally ruins it to the point that I can never buy it again. The packaging was so ridiculous and convoluted that it took me 35 minutes to get the toothbrush out and use it.}
\end{center}
\noindent
Only the three-star rating of the above review would count towards the review aggregation, while the user clearly liked the product itself, but disliked its packaging. If a potential buyer would be able to discern this, they could decide for themselves whether good packaging of the product is important to them. Amazon provides users a way to give additional star ratings on a limited number of the product's features (in this case, packaging is not one of them), but users might not be willing to take their time to repeat what they have already written down in textual form.
Clear explanations of review aggregations can also be used to improve e-commerce site recommender systems, as it has been shown that explanations can help to improve the overall acceptance of a recommender system \cite{RefWorks:doc:5e2f3970e4b0241a7d69e2a4}, and recommendations are often largely based on review aggregations such as average user ratings.
\section{Objectives}
There have already been some attempts to improve explanations for review aggregations, some of which are discussed in Chapter 2. One such attempt is what is called an Argumentative Dialogical Agent (ADA), proposed by Cocarascu et al.\ \cite{RefWorks:doc:5e08939de4b0912a82c3d46c} and implemented for the Rotten Tomatoes and Trip Advisor\footnote{https://www.tripadvisor.com/} platforms \cite{RefWorks:doc:5e0de20ee4b055d63d355913}. The goal of this project is to extend upon the work of Cocarascu et al. in order to design and implement a more generalised ADA to provide explanations for Amazon product reviews. The main objectives for the extended agent are twofold:
\begin{itemize}
\item \textbf{Generalise} the agent to work with a larger variety of different products. Currently ADA has only been implemented for movie and hotel reviews, two highly homogeneous domains in which there is not much variance in key features and review language from one product to another. Implementing ADA for Amazon reviews will require more general NLP methods in extracting review aggregations.
\item \textbf{Explore user interfaces} for the agent. While a limited argumentative dialogue has been proposed for ADA's review aggregations explanations, interfaces through which a user can partake in this dialogue have not been considered. We will implement two such interfaces, one based on text and one based on speech.
\end{itemize}
At the end of this project, we will have a working implementation of an ADA for Amazon reviews, which can be used to obtain dialogical explanations for review aggregations.
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@inproceedings{RefWorks:doc:5e349b0ce4b033832f2cb721,
author={Julian McAuley and Jure Leskovec},
year={2013},
title={Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text},
booktitle={Proceedings of the 7th ACM Conference on Recommender Systems},
series={RecSys ’13},
publisher={Association for Computing Machinery},
address={New York, NY, USA},
location={Hong Kong, China},
pages={165–172},
isbn={9781-450324090},
url={https://doi.org/10.1145/2507157.2507163},
doi={10.1145/2507157.2507163}
}
@article{RefWorks:doc:5e2f3970e4b0241a7d69e2a4,
author={Fatih Gedikli and Dietmar Jannach and Mouzhi Ge},
year={2014},
title={How should I explain? A comparison of different explanation types for recommender systems},
journal={International Journal of Human-Computer Studies},
volume={72},
number={4},
pages={367-382},
note={ID: 272548},
isbn={1071-5819},
url={http://www.sciencedirect.com/science/article/pii/S1071581913002024},
doi={https://doi.org/10.1016/j.ijhcs.2013.12.007}
}
@inproceedings{RefWorks:doc:5e2e1e23e4b0e67b35d1c360,
author={Long Jiang and Mo Yu and Ming Zhou and Xiaohua Liu and Tiejun Zhao},
year={2011},
month={jun},
title={Target-dependent Twitter Sentiment Classification},
booktitle={Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
publisher={Association for Computational Linguistics},
address={Portland, Oregon, USA},
pages={151-160,},
url={https://www.aclweb.org/anthology/P11-1016}
}
@inproceedings{RefWorks:doc:5e2e107ce4b0bc4691206e2e,
author={Li Dong and Furu Wei and Chuanqi Tan and Duyu Tang and Ming Zhou and Ke Xu},
year={2014},
month={jun},
title={Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification},
booktitle={Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
publisher={Association for Computational Linguistics},
address={Baltimore, Maryland},
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\chapter{Introduction}
In this chapter, we will first discuss the motivations behind the project and then specify its main objectives.
\section{Motivations}