Aspect Based Sentiment Analysis: A Python Demo
Summary
TLDRThis video introduces aspect-based sentiment analysis (ABSA), a method to extract structured data on aspects, opinions, and sentiments from reviews. It explains ABSA's advantages over traditional sentiment analysis and demonstrates a paper's model for extracting aspect sentiment triplets. The video includes a Python demo showcasing how to train a model on a specific dataset and evaluate its performance using F1 score.
Takeaways
- 🍽 Aspect-Based Sentiment Analysis (ABSA) focuses on identifying specific aspects of a product or service mentioned in reviews and the sentiment towards those aspects.
- 📊 ABSA provides structured data that includes aspects, opinions, and sentiments, offering more detailed insights compared to traditional sentiment analysis.
- 🔍 The example given in the script illustrates how ABSA can distinguish between positive sentiment towards 'waiters' and negative sentiment towards 'pasta' in a restaurant review.
- 📚 Terminology in ABSA includes 'aspects' or 'targets' (like 'waiters' and 'pasta'), and 'subjective opinions' (like 'friendly' and 'average').
- 📊 ABSA can quantify sentiments towards aspects by counting mentions and categorizing them as positive, negative, or neutral.
- 🔍 The script mentions clustering and dimension reduction as methods to group similar aspects when there are too many to analyze individually.
- 📖 The paper 'Learning Span-level Interactions for Aspect Sentiment Triplet Extraction' is highlighted, which introduces a model that outputs 'aspect sentiment triplets'.
- 🛠️ The model architecture involves tokenization, span enumeration, pruning, and classification to identify aspects, opinions, and their sentiment relations.
- 💻 The paper's authors have made their code publicly available on GitHub, allowing others to replicate their work and use their model.
- 🐍 A Python demo using the pre-trained model is provided, showing how to predict aspect sentiment triplets for new sentences.
- 📈 The model's performance varies depending on the dataset and encoding method used, with BERT showing better performance than LSTM.
Q & A
What is Aspect-Based Sentiment Analysis (ABSA)?
-Aspect-Based Sentiment Analysis is a method used to analyze specific aspects or targets of a product or service mentioned in a review or text, identifying the sentiment expressed towards these aspects.
How does ABSA differ from traditional sentiment analysis?
-ABSA provides more detailed insights by extracting information about specific aspects people are discussing, whether they like or dislike each aspect, and the reasons behind their sentiments, whereas traditional sentiment analysis typically focuses on overall positive, negative, or neutral sentiments.
What is an example of structured data output for ABSA?
-Structured data output for ABSA might include aspects like 'waiters' and 'pasta' from a restaurant review, with opinions such as 'friendly' and 'average', and sentiments like 'positive' and 'negative' respectively.
What terminology is used in ABSA for specific parts of a review?
-In ABSA, 'aspects' or 'targets' refer to specific parts of a product or service being reviewed, while 'subjective opinions' are the adjectives or phrases used to describe those aspects.
What does the output of ABSA look like when analyzing a large number of reviews?
-The output of ABSA on a large dataset might show the frequency of mentions for each aspect, along with the associated opinions and sentiments, such as 'food' being mentioned 523 times with various opinions like 'weird', 'authentic', 'great', and 'dry'.
How can clustering and dimension reduction be applied in ABSA?
-When there are too many aspects identified in the reviews, clustering and dimension reduction techniques can be used to group similar aspects together, simplifying the analysis and making it more manageable.
What is an 'aspect sentiment triplet' as mentioned in the paper walkthrough?
-An 'aspect sentiment triplet' refers to a combination of an aspect, the opinion expressed about that aspect, and the sentiment associated with that opinion, such as 'windows 8' being the aspect, 'did not enjoy' the opinion, and 'negative' the sentiment.
What is the process for breaking down a sentence into tokens in ABSA?
-In the model described, sentences are broken down into tokens, which are then combined to form spans. These spans represent phrases that could be aspects or opinions, like 'windows 8' or 'did not enjoy'.
How does the model handle the computational complexity of enumerating spans?
-To manage computational complexity, the model performs pruning operations that remove invalid spans and keep only candidate aspects or targets and opinions.
What is the role of the sentiment relation classifier in the ABSA model?
-The sentiment relation classifier determines whether each aspect-opinion pair conveys a positive, negative, or neutral sentiment, or if it is invalid due to being an objective fact without a subjective opinion.
How can one train a model from scratch using the provided ABSA framework?
-The provided notebook demonstrates how to train a model from scratch using one's own labeled training data and evaluate its performance using metrics like the F1 score.
Outlines
📊 Aspect-Based Sentiment Analysis Explained
This paragraph introduces Aspect-Based Sentiment Analysis (ABSA), emphasizing its ability to provide structured data that includes aspects, opinions, and sentiments. The script uses a restaurant review as an example to illustrate how ABSA can distinguish between positive and negative sentiments towards different aspects of a product or service. It also explains the terminology involved, such as 'aspects' or 'targets' for the subjects being reviewed and 'subjective opinions' for the descriptors. The paragraph concludes by highlighting the advantages of ABSA over traditional sentiment analysis, which is its capacity to extract detailed feedback on specific aspects and reasons behind the sentiments.
🔍 Deep Dive into ABSA Model Architecture
The second paragraph delves into the model architecture for ABSA, focusing on a paper titled 'Learning Span-level Interactions for Aspect Sentiment Triplet Extraction.' It outlines the process of breaking sentences into tokens and then combining them into spans to identify aspects and opinions. The model performs pruning to filter out invalid spans, leaving only potential aspects and opinions. These are then paired and classified to determine the sentiment relationship. The paragraph also discusses the model's performance, mentioning that it has shown significant improvement over previous models. It notes the use of LSTM or BERT for sentence encoding and the different datasets used for training and testing, including restaurant and laptop reviews from 2014. The paragraph concludes with a mention of a Python demo on Google Colab that demonstrates how to use the model for prediction and evaluation.
Mindmap
Keywords
💡Aspect-Based Sentiment Analysis (ABSA)
💡Structured Data
💡Aspect
💡Opinion
💡Sentiment
💡Target-Based Sentiment Analysis
💡Aspect Sentiment Triplet
💡Tokenization
💡Span
💡Pruning
💡Sentiment Relation Classifier
Highlights
Introduction to Aspect-Based Sentiment Analysis (ABSA)
ABSA outputs structured data showing aspect, opinion, and sentiment
Terminology: aspects (targets), subjective opinions, and sentiment
ABSA provides more detailed sentiment analysis compared to traditional methods
Example of ABSA output for a restaurant review
ABSA can cluster and reduce dimensions for numerous aspects
Paper walkthrough on 'Learning Span-level Interactions for Aspect Sentiment Triplet Extraction'
Model outputs aspect sentiment triplets
Model architecture involves tokenization, span enumeration, and pruning
Aspect term extraction and opinion term extraction as part of the model
Sentiment relation classifier determines the sentiment between aspect and opinion
Model performance compared to previous papers
Use of LSTM or BERT for sentence encoding
Training and testing sets from the Semantic Evaluation Workshop
Code availability on GitHub and Python demo on Google Colab
Process of downloading and installing packages for the demo
Example of training data for the 2014 laptop dataset
Pre-trained model prediction example
Model sensitivity to the dataset it was trained on
Instructions on training a model from scratch with custom data
Evaluation of model using F1 score
Call to action for likes and subscriptions
Transcripts
hello everyone today i will introduce
what is aspect-based sentiment analysis
we will take a look at what absa outputs
look like followed by a paper
walkthrough and we will end with a
python demo
let's start with an example
let's say we have an online review for a
restaurant waiters are very friendly but
the pasta is simply average
ebsa should be able to output structured
data
showing the aspect the opinion and the
sentiment
in this case waiters are being described
as friendly and it's a positive
sentiment in contrast pasta is being
described as average is a negative
sentiment
as we saw on the previous page here we
have some terminology and jargons
waiters and pasta are called aspects
also called targets
friendly and average are subjective
opinions
to summarize
absa is also called target-based
sentiment analysis and opinion mining
compared to traditional sentiment
analysis ebsa provides more details
for a given product or service ebsa can
extract what aspects are people talking
about
do people like or dislike each aspect
and for what reason
next let's look at what absa outputs
should look like
let's say we scraped all the online
reviews for a restaurant this is a
google review page for a place i really
like in florida
the outputs should look something like
this for instance people may have
mentioned the word food 523 times
seven people may have said the food
tasted weird
six people may have said the food tasted
authentic
43 said the food was great 17 said the
food was dry and weir correspond to a
negative sentiment
authentic positive great positive
and so on and on the other hand people
may have mentioned the word service 326
times 34 people may have said the
service was slow
12 may have said the service was fast
six may have said the servers were rude
and 15
friendly they also correspond to
different sentiments
if you ended up having too many aspects
you can do clustering and dimension
reduction to group similar aspects
together
and now that we have a general
expectation of what outputs we're
looking for i'm going to do a paper walk
through so that we can understand the
model design and architecture
this paper is called learning span level
interactions for aspect sentiment
triplet extraction it has two first
authors who contributed to the work
equally
and their model will output what they
call
aspect sentiment triplets
for example we have a consumer review
here for a laptop did not enjoy the new
windows 8 and touch screen functions
here windows 8 is the aspect and so is
touch screen functions
they were both described by the opinion
not enjoy and the sentiment is negative
here we have the model architecture
first they break a sentence into tokens
so did not enjoy the new windows 8 they
were broken down into many tokens
and these tokens are then combined to
form a span
for example did
did not enjoy not enjoy enjoy the new
and we need to combine these words to
form a span because an aspect or an
opinion can be a phrase and a phrase can
have multiple words windows 8 should be
read together as one phrase it wouldn't
make sense to break it apart
and the enumeration of span is pretty
computational intensive because the
number of possible spans grows
exponentially as the sentence gets
longer
to control the computational complexity
next they did some pruning
here is a task called aspect term
extraction
and opinion term extraction
each span is then classified as either
invalid
an opinion
or a target also called an aspect
and a pruning operation here removes
everything that's invalid and only keeps
candidate aspects or targets as well as
candidate opinions
and finally each aspect is paired with
each opinion these target candidate
aspects and opinions are coupled
together so that we can determine the
sentiment relationship between them
here the sentiment relation classifier
determines whether each aspect opinion
pair
conveys a positive
or a negative or a neutral sentiment it
can also be invalid because the new
windows 8 is a fact it's not a
subjective opinion but rather an
objective fact so there is no sentiment
relationship there
and this model has a decent performance
which is significantly improved compared
to previous papers
the performance varies depending on the
model in the beginning they can either
do sentence encoding using by lstm or
bird
bird is a more complex model so it leads
to better performance
they also had four different training
and testing sets
here rest 14 means that it's a data set
about restaurant from 2014
and lab 14 means that it's a data set
about laptop from 2014.
these data sets were originally released
by the semantic evaluation workshop
the authors of this paper have made
their code publicly available on github
and we can see a python demo on google
colab
in the beginning we need to download and
install a number of packages as well as
loading the pre-trained model
there are a lot of packages that needed
to be downloaded it took me about four
minutes
the next cell shows what training data
looks like and here specifically they're
showing an example
for the 2014 laptop data
and this is what the data looks like for
example i charge it at night and skip
taking the chord with me because of the
good battery life
the target word is battery life and its
index goes from 16 to 17
and the opinion word is good its index
goes from 15 to 15
and also this is a
positive sentiment
and below we can use a pre-trained model
for prediction this is the training
example we saw from the paper did not
enjoy the new windows 8 and touch screen
functions
at the very end the output is going to
show you a triplet
so here the target word is windows 8 and
the opinion is not enjoy the sentiment
is negative this sentence has two
aspects so we also get an output for
touch screen
so the touch screen functions
the opinions also not enjoy and the
sentiment is negative
now let's try providing a new example
easy to use and set up
no problems after six months
and let's run the cell and
see what happens
and after about 48 seconds we have the
output
here the target is used the opinion is
that it's easy to use and the sentiment
is positive
so it looks pretty good
and let's try something else
so so this model is trained on laptop
data let's try
something not related to laptop
let's say the treatment is effective
and i already run this cell it ended up
not giving us anything
meaning that it wasn't able to detect
any sentiment aspect triplets
so the model is pretty sensitive to the
data set it was trained on because it
was trained on data about laptop it
wasn't able to detect
it wasn't able to detect anything
in the medical domain
but the nice thing is that the notebook
also show you how to train a model from
scratch using your own label training
data
they also show you how to evaluate
your model f1 score
this is the end of the video if you
learned anything useful today feel free
to like and subscribe
thank you for your time and take care
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