I have run text through a sentiment analysis model, and it gives me 3 probabilities for if the text is Negative, Neutral or Positive e.g.
[Negative = 0.38]
[Neutral = 0.42]
[Positive = 0.20]
I am wanting to convert this to a score so that each text string has a score with higher scores meaning the text is more positive. I was thinking of just doing [Positive - Negative] and ignoring the neutral case to generate a score, but wondered if there were any better ideas as this seems overly simple.
I want to generate a score so I can run regressions on these scores for predictive purposes.
Doing that sum on my dataset gets me a distribution that looks like this: https://i.imgur.com/DewzBRM.jpeg
(Sorry for not embedding, I don't have the 'reputation' required to do so).
This seems pretty decent, but I was curious if anyone had other alternative ideas.
Thanks in advance
Related
I have a sentiment analysis dataset that is labeled in three categories: positive, negative, and neutral. I also have a list of words (mostly nouns), for which I want to calculate the sentiment value, to understand "how" (positively or negatively) these entities were talked about in the dataset. I have read some online resources like blogs and thought about a couple of approaches for calculating the sentiment score for a particular word X.
Calculate how many data instances (sentences) which have the word X in those, have "positive" labels, have "negative" labels, and "neutral" labels. Then, calculate the weighted average sentiment for that word.
Take a generic untrained BERT architecture, and then train it using the dataset. Then, pass each word from the list to that trained model to get the sentiment scores for the word.
Does any of these approaches make sense? If so, can you suggest some related works that I can look at?
If these approaches don't make sense, could you please advise how I can calculate the sentiment score for a word, in a given dataset?
The first method will suffer from the same drawbacks as other bag-of-words models do. Consider that you have a dataset of movie reviews with their sentiment scores, and you want to find the sentiment for a particular actor called X. A label for a sample like "X's acting was the only good thing in an otherwise bad movie" will be negative, but the sentiment towards X is positive. A simple approach like the first one can't handle such cases.
The second approach also does not make much sense, as the BERT models may not perform well without context. You can try using weakly supervised learning which can help in creating token-level labels. Read section 3.3 for this paper to get an idea about this. Disclaimer: I'm one of the authors of this paper.
I’m trying to check the performance of my LDA model using a confusion matrix but I have no clue what to do. I’m hoping someone can maybe just point my in the right direction.
So I ran an LDA model on a corpus filled with short documents. I then calculated the average vector of each document and then proceeded with calculating cosine similarities.
How would I now get a confusion matrix? Please note that I am very new to the world of NLP. If there is some other/better way of checking the performance of this model please let me know.
What is your model supposed to be doing? And how is it testable?
In your question you haven't described your testable assessment of the model the results of which would be represented in a confusion matrix.
A confusion matrix helps you represent and explore the different types of "accuracy" of a predictive system such as a classifier. It requires your system to make a choice (e.g. yes/no, or multi-label classifier) and you must use known test data to be able to score it against how the system should have chosen. Then you count these results in the matrix as one of the combination of possibilities, e.g. for binary choices there's two wrong and two correct.
For example, if your cosine similarities are trying to predict if a document is in the same "category" as another, and you do know the real answers, then you can score them all as to whether they were predicted correctly or wrongly.
The four possibilities for a binary choice are:
Positive prediction vs. positive actual = True Positive (correct)
Negative prediction vs. negative actual = True Negative (correct)
Positive prediction vs. negative actual = False Positive (wrong)
Negative prediction vs. positive actual = False Negative (wrong)
It's more complicated in a multi-label system as there are more combinations, but the correct/wrong outcome is similar.
About "accuracy".
There are many kinds of ways to measure how well the system performs, so it's worth reading up on this before choosing the way to score the system. The term "accuracy" means something specific in this field, and is sometimes confused with the general usage of the word.
How you would use a confusion matrix.
The confusion matrix sums (of total TP, FP, TN, FN) can fed into some simple equations which give you, these performance ratings (which are referred to by different names in different fields):
sensitivity, d' (dee-prime), recall, hit rate, or true positive rate (TPR)
specificity, selectivity or true negative rate (TNR)
precision or positive predictive value (PPV)
negative predictive value (NPV)
miss rate or false negative rate (FNR)
fall-out or false positive rate (FPR)
false discovery rate (FDR)
false omission rate (FOR)
Accuracy
F Score
So you can see that Accuracy is a specific thing, but it may not be what you think of when you say "accuracy"! The last two are more complex combinations of measure. The F Score is perhaps the most robust of these, as it's tuneable to represent your requirements by combining a mix of other metrics.
I found this wikipedia article most useful and helped understand why sometimes is best to choose one metric over the other for your application (e.g. whether missing trues is worse than missing falses). There are a group of linked articles on the same topic, from different perspectives e.g. this one about search.
This is a simpler reference I found myself returning to: http://www2.cs.uregina.ca/~dbd/cs831/notes/confusion_matrix/confusion_matrix.html
This is about sensitivity, more from a science statistical view with links to ROC charts which are related to confusion matrices, and also useful for visualising and assessing performance: https://en.wikipedia.org/wiki/Sensitivity_index
This article is more specific to using these in machine learning, and goes into more detail: https://www.cs.cornell.edu/courses/cs578/2003fa/performance_measures.pdf
So in summary confusion matrices are one of many tools to assess the performance of a system, but you need to define the right measure first.
Real world example
I worked through this process recently in a project I worked on where the point was to find all of few relevant documents from a large set (using cosine distances like yours). This was like a recommendation engine driven by manual labelling rather than an initial search query.
I drew up a list of goals with a stakeholder in their own terms from the project domain perspective, then tried to translate or map these goals into performance metrics and statistical terms. You can see it's not just a simple choice! The hugely imbalanced nature of our data set skewed the choice of metric as some assume balanced data or else they will give you misleading results.
Hopefully this example will help you move forward.
I am trying to build a model on a class imbalanced dataset (binary - 1's:25% and 0's 75%). Tried with Classification algorithms and ensemble techniques. I am bit confused on below two concepts as i am more interested in predicting more 1's.
1. Should i give preference to Sensitivity or Positive Predicted Value.
Some ensemble techniques give maximum 45% of sensitivity and low Positive Predicted Value.
And some give 62% of Positive Predicted Value and low Sensitivity.
2. My dataset has around 450K observations and 250 features.
After power test i took 10K observations by Simple random sampling. While selecting
variable importance using ensemble technique's the features
are different compared to the features when i tried with 150K observations.
Now with my intuition and domain knowledge i felt features that came up as important in
150K observation sample are more relevant. what is the best practice?
3. Last, can i use the variable importance generated by RF in other ensemple
techniques to predict the accuracy?
Can you please help me out as am bit confused on which w
The preference between Sensitivity and Positive Predictive value depends on your ultimate goal of the analysis. The difference between these two values is nicely explained here: https://onlinecourses.science.psu.edu/stat507/node/71/
Altogether, these are two measures that look at the results from two different perspectives. Sensitivity gives you a probability that a test will find a "condition" among those you have it. Positive Predictive value looks at the prevalence of the "condition" among those who is being tested.
Accuracy is depends on the outcome of your classification: it is defined as (true positive + true negative)/(total), not variable importance's generated by RF.
Also, it is possible to compensate for the imbalances in the dataset, see https://stats.stackexchange.com/questions/264798/random-forest-unbalanced-dataset-for-training-test
I am using Spark ML to optimise a Naive Bayes multi-class classifier.
I have about 300 categories and I am classifying text documents.
The training set is balanced enough and there is about 300 training examples for each category.
All looks good and the classifier is working with acceptable precision on unseen documents. But what I am noticing that when classifying a new document, very often, the classifier assigns a high probability to one of the categories (the prediction probability is almost equal to 1), while the other categories receive very low probabilities (close to zero).
What are the possible reasons for this?
I would like to add that in SPARK ML there is something called "raw prediction" and when I look at it, I can see negative numbers but they have more or less comparable magnitude, so even the category with the high probability has comparable raw prediction score, but I am finding difficulties in interpreting this scores.
Lets start with a very informal description of Naive Bayes classifier. If C is a set of all classes and d is a document and xi are the features, Naive Bayes returns:
Since P(d) is the same for all classes we can simplify this to
where
Since we assume that features are conditionally independent (that is why it is naive) we can further simplify this (with Laplace correction to avoid zeros) to:
Problem with this expression is that in any non-trivial case it is numerically equal to zero. To avoid we use following property:
and replace initial condition with:
These are the values you get as the raw probabilities. Since each element is negative (logarithm of the value in (0, 1]) a whole expression has negative value as well. As you discovered by yourself these values are further normalized so the maximum value is equal to 1 and divided by the sum of the normalized values
It is important to note that while values you get are not strictly P(c|d) they preserve all important properties. The order and ratios are exactly (ignoring possible numerical issues) the same. If none other class gets prediction close to one it means that, given the evidence, it is a very strong prediction. So it is actually something you want to see.
I'm designing a text classifier in Python using NLTK. One of the features considered in every sentence is it's sentiment. I want to weight sentences with either positive or negative sentiments more that those without any sentiment(neutral sentences). Using the movie review corpus along with the naive bayes classifier results in only positive and negative labels. I tried using demo_liu_hu_lexicon in nltk.sentiment.utils but the function does not return any values but instead prints it to the output and is very slow. Does anyone know of a library which gives some sort of weight to sentences based on sentiment?
Thanks!
Try the textblob module:
from textblob import TextBlob
text = '''
These laptops are horrible but I've seen worse. How about lunch today? The food was okay.
'''
blob = TextBlob(text)
for sentence in blob.sentences:
print(sentence.sentiment.polarity)
# -0.7
# 0.0
# 0.5
It uses the nltk library to determine the polarity - which is a float measure ranging from -1 to 1 for the sentiment. Neutral sentences have zero polarity. You should be able to get the same measure directly from nltk.
Vader is a rule-based sentiment analysis tool that works well for social media texts as well regular texts.
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyser = SentimentIntensityAnalyzer()
def print_sentiment_scores(tweets):
vadersenti = analyser.polarity_scores(tweets)
return pd.Series([vadersenti['pos'], vadersenti['neg'], vadersenti['neu'], vadersenti['compound']])
text = 'This goes beyond party lines. Separating families betrays our values as Texans, Americans and fellow human beings'
print_sentiment_scores(text)
The results are:
0 0.2470
1 0.0000
2 0.7530
3 0.5067
The compound score is computed by summing the valence scores of each word in the lexicon, adjusted according to the rules, and then normalized to be between -1 (most extreme negative) and +1 (most extreme positive). This is the most useful metric if you want a single unidimensional measure of sentiment for a given sentence. Calling it a 'normalized, weighted composite score' is accurate
Though positive sentiment is derived with the compound score >= 0.05, we always have an option to determine the positive, negative & neutrality of the sentence, by changing these scores
I personally find Vader Sentiment to figure out the sentiment based on the emotions, special characters, emojis very well.