=Paper=
{{Paper
|id=Vol-1520/paper29
|storemode=property
|title=Aspect-based Sentiment Analysis for Social Recommender System
|pdfUrl=https://ceur-ws.org/Vol-1520/paper29.pdf
|volume=Vol-1520
|dblpUrl=https://dblp.org/rec/conf/iccbr/Chen15
}}
==Aspect-based Sentiment Analysis for Social Recommender System==
https://ceur-ws.org/Vol-1520/paper29.pdf
262
Aspect-based Sentiment Analysis for Social
Recommender Systems
Yoke Yie Chen
IDEAS Research Institute,
Robert Gordon University,
Aberdeen, Scotland
{y.y.chen}@rgu.ac.uk
1 Introduction
Social recommender systems provide users with a list of recommended items by
exploiting knowledge from social content. Representation, similarity and rank-
ing algorithms from the Case-Based Reasoning (CBR) community have naturally
made a significant contribution to social recommender systems research [1, 2].
Recent works in social recommender systems have been focused on learning im-
plicit preferences of users from online consumer reviews. Most online reviews
contain user opinion in the form of positive and negative sentiment on multiple
aspects of the product. Since a product may have multiple aspects, we hypoth-
esize that users purchase choices are based on comparison of products; which
implicitly or explicitly involves comparison of aspects of these products. There-
fore, our main research question is “Does considering product aspects importance
(weight) improve prediction accuracy of a product ranking algorithm?”
2 Research Aim
This research aims to develop a novel aspect-based sentiment scoring algorithm
for social recommender systems. Our particular focus will be on using social con-
tent to develop novel algorithms for di↵erent product domains. For this purpose,
we intend to:
1. Develop an aspect extraction algorithm to extract product aspects.
2. Develop aspect weighting algorithms to extract product aspects weights from
social content.
3. Study the e↵ect of temporal dynamics on aspect weight.
4. Evaluate the performance of our proposed algorithms in performing a top-
N recommendation task using standard performance metrics such as mean
average precision.
Copyright © 2015 for this paper by its authors. Copying permitted for private and
academic purposes. In Proceedings of the ICCBR 2015 Workshops. Frankfurt, Germany.
263
2 Yoke Yie Chen
3 Challenges
Social recommender system harness knowledge from product reviews to generate
better recommendation. Key to this task is the need for a novel aspect based sen-
timent analysis approach to harness this large volume of information. However,
this approach su↵ers three main challenges:
1. Aspects extracted from product reviews using NLP-based techniques rely
on POS tagging and syntactic parsing which are known to be less robust
when applied to informal text. As a result, it is not unusual to have a large
numbers of spurious content to be extracted incorrectly as aspects.
2. A user’s purchase decision hints at the aspects that are likely to have influ-
enced their decision and as such be deemed more important. To understand
the importance of an aspect to users, it is necessary to further reveal the
importance weight that users placed on an aspect. Additionally, user prefer-
ences change over time. Term frequency (TF) is the naive approach for this
task where the weight of an aspect is equal to the number of occurrences of
that aspect in product reviews. However, this approach is not able to capture
users’ preferences that change over time.
3. The absence of ground truth data causes evaluating ranking algorithm a
challenging task in recommender system. For example, Best Seller ranking in
Amazon can be a straightforward reference to evaluate the ranking of system
generated recommendation list. However, this ranking is biased towards old
products in Amazon. Therefore, there is a need to study relevant knowledge
sources to construct a reference ranking for evaluation purpose.
4 Proposed Plan of Research
To answer our research question, our proposed plan of research is:
1. Compare the performance of our proposed aspect extraction algorithm with
key state-of-the-art algorithms to determine the impact of aspect quality on
recommendation tasks. We will evaluate the performance of these algorithms
through accuracy metrics in extracting genuine product aspects. Thereafter,
we apply the extracted aspects from these approaches in our aspect based
sentiment scoring algorithm and rank the products. We then compare the
recommendation performance of aspect based sentiment scoring algorithm
with a sentiment analysis algorithm that is agnostic of aspects.
2. Feature selection techniques in machine learning are known to enhance ac-
curacy in supervised learning tasks such as text classification by identifying
redundant and irrelevant features. We propose to explore di↵erent feature se-
lection techniques (e.g. Information Gain and Chi-squared) to select aspects
that are important to users.
3. Our initial approach in aspect weighting algorithm places individual prod-
uct aspect with equal importance weight across all products. We intend to
264
Aspect-based Sentiment Analysis for Social Recommender Systems 3
explore other related approaches such as TF-IDF (Term Frequency Inverse
Document Frequency) to represent the importance of a product aspect. TF-
IDF has been widely used in Information Retrieval community to evaluate
the importance of a word to a document in a corpus. We propose to augment
our aspect weighting algorithm by evaluating the importance of a product
aspect to a particular product.
4. To study the e↵ect of temporal dynamics in aspect importance weight, we
look into investigating aspect weights that are inferred by:
– Trending information. We would like to analyse di↵erent trending
patterns of aspects occurrence in product reviews over the years (e.g.
upward, downward and recurring trend). Specifically, a higher weight
should be given to aspects which have an upward and recurring trend,
indicating that the importance of an aspect is growing. Likewise, a lower
weight should be given to aspects having a downward trend.
– Recency of aspects. Aspects which frequently appear in old product
reviews will have a lower weight than aspects appearing in recent prod-
uct reviews. This indicates that aspects that are frequently occurring in
recent product reviews are deemed important.
5. To evaluate our ranking algorithm, we use users’ ratings as the baseline
to compare with our proposed ranking approach. This baseline ranks each
product using the average users’ rating. Products in the higher rank are thus
recommended.
5 Current Progress
Designed and developed novel algorithms in the following areas:
– Aspect extraction. The proposed approach integrates semantic relation-
ship and frequency cut-o↵. The proposed approach was evaluated against
state-of-the-art techniques and obtained positive results.
– Aspect selection. We address the problem of selecting important aspects
using feature selection heuristics based on frequency counts and Information
Gain (IG) to rank and select the most useful aspects.
– Aspect-based sentiment scoring. The proposed algorithm incorporates
aspect importance weight and sentiment distribution. We investigated two
di↵erent resources that infer the importance of product aspects: preference
and time.
References
1. R. Dong, M. Schaal, M. OMahony, K. McCarthy, and B. Smyth. Opinionated
product recommendation. In Inter. Conf. on Case-Based Reasoning. 2013.
2. L. Quijano-Sánchez, D. Bridge, B. Dı́az-Agudo, and J. Recio-Garcı́a. Case-based
aggregation of preferences for group recommenders. In Case-Based Reasoning Re-
search and Development, pages 327–341. 2012.