This document summarizes research on grouping customer reviews using unsupervised machine learning. The research analyzed why some positively or negatively labeled reviews were incorrectly clustered. It found that the importance of words in incorrectly clustered reviews was often more similar to words in the opposite cluster than the correct one. Specifically, words with low frequencies but high importance similarities across clusters misled the clustering algorithm. Improving the handling of insignificant but misleading words could enhance clustering accuracy.
Analyzing Text Preprocessing and Feature Selection Methods for Sentiment Anal...Nirav Raje
This was a research project for an undergraduate academic seminar. Analyzed the impact of various text preprocessing techniques, feature weighting (FF, FP, TF-IDF), feature selection (filters, wrappers, embedded), lemmatization, tokenization (unigram, bigram and 1-to-3-gram) on 3 open Twitter datasets.
Explore the power of Natural Language Processing (NLP) and Data Science in uncovering valuable insights from Flipkart product reviews. This presentation delves into the methodology, tools, and techniques used to analyze customer sentiments, identify trends, and extract actionable intelligence from a vast sea of textual data. From understanding customer preferences to improving product offerings, discover how NLP Data Science is revolutionizing the way businesses leverage consumer feedback on Flipkart. Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Analyzing Arguments during a Debate using Natural Language Processing in PythonAbhinav Gupta
This presentation will guide you through the application of Python NLP Techniques to analyze arguments during a debate and define a strategy to figure out the winner of the debate on the basis of strength and relevance of the arguments.
This is made for PyCon India 2015.
For details : https://in.pycon.org/cfp/pycon-india-2015/proposals/analyzing-arguments-during-a-debate-using-natural-language-processing-in-python/
Contact me : abhinav.gpt3@gmail.com
Measuring Semantic Similarity and Relatedness in the Biomedical Domain : Methods and Applications - presented Feb 21, 2012 as a webinar to the Mayo Clinic BMI group.
Analyzing Text Preprocessing and Feature Selection Methods for Sentiment Anal...Nirav Raje
This was a research project for an undergraduate academic seminar. Analyzed the impact of various text preprocessing techniques, feature weighting (FF, FP, TF-IDF), feature selection (filters, wrappers, embedded), lemmatization, tokenization (unigram, bigram and 1-to-3-gram) on 3 open Twitter datasets.
Explore the power of Natural Language Processing (NLP) and Data Science in uncovering valuable insights from Flipkart product reviews. This presentation delves into the methodology, tools, and techniques used to analyze customer sentiments, identify trends, and extract actionable intelligence from a vast sea of textual data. From understanding customer preferences to improving product offerings, discover how NLP Data Science is revolutionizing the way businesses leverage consumer feedback on Flipkart. Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Analyzing Arguments during a Debate using Natural Language Processing in PythonAbhinav Gupta
This presentation will guide you through the application of Python NLP Techniques to analyze arguments during a debate and define a strategy to figure out the winner of the debate on the basis of strength and relevance of the arguments.
This is made for PyCon India 2015.
For details : https://in.pycon.org/cfp/pycon-india-2015/proposals/analyzing-arguments-during-a-debate-using-natural-language-processing-in-python/
Contact me : abhinav.gpt3@gmail.com
Measuring Semantic Similarity and Relatedness in the Biomedical Domain : Methods and Applications - presented Feb 21, 2012 as a webinar to the Mayo Clinic BMI group.
Online feedback correlation using clusteringawesomesos
My presentation given for Internet search class. I theorized that you could determine how good a product was based on the different types of negative reviews automatically
High level introduction to text mining analytics, which covers the building blocks or most commonly used techniques of text mining along with useful additional references/links where required for background/literature and R codes to get you started.
A Review on Subjectivity Analysis through Text Classification Using Mining Te...IJERA Editor
The increased use of web for expressing ones opinion has resulted in to an enhanced amount of subjective content available in the Web. These contents can often be categorized as social content like movie or product reviews, Customer Feedbacks, Blogs, Communication exchange in discussion forums etc. Accurate recognition of the subjective or sentimental web content has a number of benefits. Understanding of the sentiments of human masses towards different entities and products enables better services for contextual advertisements, recommendation systems and analysis of market trends. The objective behind framing this paper to analyze various sentiment based classification techniques which can be utilized for quick estimation of subjective contents of Political reviews based on politicians speech. The paper elaborately discusses supervised machine learning algorithm: Naïve Bayes classification and compares its overall accuracy, precisions as well as recall values.
1. + František Dařena
Jan Žižka
Department
of
Faculty of
Business
Karel Burda Informatics and
Economics
Mendel Czech
University Republic
in Brno
Grouping Customer Opinions Written in
Natural Language Using Unsupervised
Machine Learning
2. +
Introduction
Many companies collect opinions expressed by
their customers
These opinions can hide valuable knowledge
Discovering such the knowledge by people can
be a very demanding task because:
the opinion database can be very large,
the customers can use different languages,
the people can handle the opinions subjectively,
sometimes additional resources (like lists of positive
and negative words) might be needed.
3. +
Introduction
Ourprevious research focused on the analysis
what was significant for including a certain
opinion into one of categories like satisfied or
dissatisfied customers
However, this requires to have the reviews
separated into classes sharing a common
opinion/sentiment
4. +
Introduction
Clusteringas the most common form of
unsupervised learning enables automatic
grouping of unlabeled documents into subsets
called clusters
Inthe previous research, we analyzed how well
a computer can separate the classes
expressing a certain opinion and to find a
clustering algorithm with a set of its best
parameters, similarity, and clustering-criterion
function, word representation, and the role of
stemming for the given specific data
5. +
Objective
Clustering process is naturally not errorless
and some reviews labeled as positive appear
in a cluster containing mostly negative
reviews and vice versa
The objective was to analyse why certain
reviews were assigned “wrongly” to a group
containing mostly reviews from a different
class in order to improve the results of
classification and prediction
6. +
Data description
Processed data included reviews of hotel clients
collected from publicly available sources
The reviews were labeled as positive and
negative
Reviews characteristics:
more than 5,000,000 reviews
written in more than 25 natural languages
written only by real customers, based on their
experience
written relatively carefully but still containing errors that
are typical for natural languages
7. +
Properties of data used for
experiments
The subset (marked as written in English) used in
our experiments contained almost two million
opinions
Review category Positive Negative
Number of reviews 1,190,949 741,092
Maximal review length 391 words 396 words
Average review length 21.67 words 25.73 words
Variance 403.34 words 618.47 words
8. +
Review examples
Positive
The breakfast and the very clean rooms stood out as the best
features of this hotel.
Clean and moden, the great loation near station. Friendly
reception!
The rooms are new. The breakfast is also great. We had a really
nice stay.
Good location - very quiet and good breakfast.
Negative
High price charged for internet access which actual cost now is
extreamly low.
water in the shower did not flow away
The room was noisy and the room temperature was higher than
normal.
The air conditioning wasn't working
9. +
Data preparation
Datacollection, cleaning (removing tags, non-letter
characters), converting to upper-case
Removing stopwords and words shorter than 3
characters and
Spell checking, diacritics removal etc. were not carried
out
Creatingthree smaller subsets containing positive and
negative reviews with the following proportions:
about 1,000 positive and 1,000 negative (small)
about 50,000 positive and 50,000 negative (medium)
about 250,000 positive and 250,000 negative (large)
10. +
Experimental steps
Transformation of the data into the vector representation (bag-
of-words model, tf-idf weighting schema)
Clustering with Cluto* with following parameters
similarity function – cosine similarity,
clustering method – k-means (Cluto’s variation)
criterion function that is optimized during clustering process – H2
weighted entropy of the results varied from about 0.58 to 0.60
(e.g., for the small set of reviews, the entropy was 0.587, and
accuracy 0.859)
* Free software providing different clustering methods working with several
clustering criterion functions and similarity measures, suitable for operating on
very large datasets.
11. +
Graphical representation of the
results of clustering
False Positive (FP) False Negative (FP)
True Positive (TP) True Negative (TN)
Clustered Positive (CP) Clustered Negative (CN)
12. +
Analysis of incorrectly clustered
reviews
When a review rPi, originally labeled as positive, is
“wrongly” assigned to a cluster with mostly negative
reviews (CN), we can assume that the properties of this
review are more “similar” to the properties of the other
reviews in CN, i.e., the words of rPi and their combinations
are more similar to the words contained in the dictionary
of CN
The similarity was related to the frequency of words of rPi
in the subsets of the clustering solution (FN is compared
to TN, TP, CP, and FP is compared to TP, TN, CN)
13. +
Analysis of incorrectly clustered
reviews
We 𝑋
introduce the importance 𝑖 𝑤 𝑖 of a word wi in a
given set X:
where NX(wi) is the frequency of word wi in set X and
NX is the number of dictionary words in X
14. +
Analysis of incorrectly clustered
reviews
The importance of a word in one set should be similar
to the importance of the same word in the most similar
set, i.e., importance of words in FN and TN should be
more similar than, e.g., importance of words in FN and
TP
Lowest value among
and corresponds to highest
importance similarity with TP, TN, or CN
The same comparisons between FN and TN, TP, and
CP were carried out
15. + Importance of words from dictionary of False
Positive set compared to the other sets
16. + Importance of words from dictionary of False
Negative set compared to the other sets
17. +
Results of the analysis
The words with higher frequencies included mostly the words
that could be considered positive (e.g., location, excellent, or
friendly) and negative (e.g., small, noise, or poor) in terms of
their contribution to the assignment of reviews to a “correct”
category
These words that are important from the correct classification
viewpoint have often the most similar importance in a different
set than one would expect, e.g., some words in reviews from
FN bearing a strong positive sentiment had their importance
most similar to their importance in TN and not in TP or CP
18. +
Example 1 – small data set
A strongly positive word excellent was used 3 times in the
FN (290 positive reviews, 3,678 words) iFN = 0.0008
Such the importance was the most similar to the
importance of the same word in TN (iTN = 0.0007) and not
in TP (iTP = 0.007) or CP (iCP = 0.006)
Review “Excellent bed making. Very good restaurant but
an English language menu would be advantageous to
non-german speaking visitors.” containing a strongly
positive word excellent was categorized incorrectly
19. +
Example 2 – small data set
A positiveword good (with smaller positivity than
excellent) had the importance iFN = 0.0114
Suchthe importance was most similar to the
importance of the same word in CP (iCP = 0.0146)
and not in TP (iTP = 0.016) or TN (iTN = 0.0021)
Nevertheless,
some reviews containing this positive
word were assigned to a group with mostly negative
reviews.
20. +
Results of the analysis
Both examples demonstrate that other document
properties, i.e., the presence of the other words together
with their importance, are signifi-
cant. This is demonstrated in the
table with importance similarities
of words of an obviously positive
review containing twice strongly
positive word “good” which was
assigned incorrectly to CN.
21. +
Results of the analysis – importance
vs. frequency
The analysis of the importance of words from dictionary of FN
showed that about 60% of words had their importance similar
to their importance in TN
However, the frequency of each of these words (number of
occurrences in all reviews) was relatively low (many of them
appeared just once)
These words with highly similar importance also often did not
bear any sentiment, such as the words discounted, happening,
or attitude
22. +
Conclusions
The study aimed at finding what was actually the reason of
assigning some documents into a “wrong” class
The critical information is provided by certain significant words
included in individual reviews
Words that the previous research found significant for opinion
polarity did not take effect as misleading information unlike
words that were much more or quite insignificant
Specific words (or their combinations) can be filtered out as
noise, improving the cluster generation