Sentiment analysis : Frequency-based models

We give our tenants insights about their online reputation based on their online reviews and ratings. In doing so, one thing we try to do is pull apart the text of reviews to understand what the reviews are dealing with, and tell our clients what their customers are talking about and how happy those customers are with key aspects of our clients’ business.

So for example, we might identify 100 reviews for our client mentioning price, and leveraging the star rating of those reviews, we might discern that 80% of those reviews are positive and the average rating of those reviews is 4.0 stars. However, this method could be improved: a positive review mentioning price is not necessarily positive about price. For example:

The food was awesome, and the service absolutely excellent. The price was very high for a coffee-shop style restaurant.

This 5 star review is obviously negative about the price of the restaurant. We need a model that tells us the local sentiment of a sentence or a subsentence in order to be able to understand what elements drive the rating of the review. I’ll explain some of the techniques we have studied, implemented and benchmarked in order to build our Sentiment Mining Tool.

Naive Bayes Classifier

Naive Bayes is the first and the easiest method to classify sentiment in a text. It’s based on the Bayes formula for conditional probabilities:

Bayes Formula

 

 

We’ll represent a text by a Bag of Words, which is a set of features “the word w appears f times” for each word w in the sentence and f, the frequency of w in the sentence. Assuming the Naive Bayes assumption that these features are independent, this formula helps us deduce the probability that the sentence is positive (A) knowing that w appears f times (B) for every w. In fact, we can deduce from the frequencies in a large enough dataset the probability for a sentence to be positive (A), and the probabilities of every feature and then of their intersection (B). Training the model on a training set of 10,000 annotated sentences, we get a set of informative features that are helpful to predict whether a sentence is positive or negative. Here are the 10 most informative features we get:

Naive Bayes sentiment-bearing keywords

Naive Bayes classifier’s informative features


This method is the easiest to implement and the big advantage is that it’s completely transparent. When we process it, we know that the classifier found a set of strongly positive or of strongly negative words, and that it is why we classified the sentence in such a way.

How to improve it

However, there are several drawbacks using this method.

First, it fails to identify the neutral class. As a matter of fact, words can have a positive or a negative meaning (“good”, “awesome”, ”horrible”, …) but no word has a neutral connotation. Often, it’s all about the absence of such positively or negatively meaningful words or about the structure of the sentence that reflects the absence of strong emotion. The Bag of Words representation doesn’t address this problem.

It also fails to understand intensity and negations. Comparing “good” and “quite good” for instance, the first one is more likely to appear in a positive sentence than the second one. We tried some methods to address this: adding a list of meaningful bigrams (which mean that we would read “quite good” as a single word for instance), or training the model on bigrams instead of training it on single words, but both didn’t improve our model very much. We also fail to identify negations most of the time, because this model doesn’t take the word order into account.

Most of all, the Naive Bayes model doesn’t perform very well in solving the local sentiment analysis problem. In a long text, having a high frequency of positive words: “sensational”, “tasty”, … makes it very likely that the author is expressing positive sentiment. But as our goal is to determine the local sentiment, we want to process the tool on short sentences and subsentences. (We already have a star rating that tells us the author’s overall sentiment.) We don’t have enough words in the sentence to aggregate so we need to understand very precisely the semantic structure.

The Bag of Words representation is a very bad way to do this. For instance, the sentence “The food could have been more tasty.”, we detect the word “tasty” that is related to a positive feeling, but we don’t understand that “could have been more” is a kind of negation or nuance. Many short sentences are like that, and looking at only a small sentence dataset reduced our accuracy from around 77% to less than 65%.

Rule-based sentiment models

To improve the Naive Bayes methods and make it fit the short sentences sentiment analysis challenge, we added some rules to take into account negations, intensity markers (“more”, “extremely”, “absolutely”, “the most”, …), nuance, and other semantic structures that appear very often near sentimental phrases and change their meanings. For instance, in “The food wasn’t very tasty”, we want to understand that “not very tasty” is less negative than “not tasty” or “not tasty at all”.

We leveraged the results of the Naive Bayes training to build a large vocabulary of positive and negative words. When we process a given sentence, we attribute every word a positive and a negative score, and calculate the overall scores by a precise analysis of the semantical structure based on the open-source library spacy’s pipelines for part-of-speech tagging and dependency parsing. We get a metric for positive, negative and neutral scores, the neutral score being defined as the proportion of words that are neither positive nor negative in the sentence. We used a deep-learning technique to deduce from our training set the relation between these scores and the sentiment. Here are the graphs we obtained for negative, neutral and positive sentences:

Sentiment scores for negative sentencesSentiment scores for neutral sentencesSentiment scores for positive sentences

The model helps us decide very well whether an expressive sentence is positive or negative (we get around 75% accuracy), but struggles understanding a criteria for neutrality or absence of sentiment (on our test-set, it’s wrong 80% of the time). It’s much better than the Naive Bayes, but 75% is less than the state-of-art for positive/negative decision.


Semantic Convolution with Word2Vec

At Reputation.com, we work with millions of online reviews from hundreds of sources. One of the unusual characteristics of reviews compared to the vast majority of text corpora is that, almost by definition, reviews are structured in such a way that they can be categorized (in one or many dimensions depending on the review site and/or industry). However, we often find ourselves doing text classification/tagging on topics that are not already labeled by the review site.  This article is an informal introduction to a set of techniques we have developed to leverage existing unlabeled corpora in conjunction with the labeled data. In particular, we present a semi-supervised learning algorithm for multi-label text classification.

In recent years, a lot of text classification projects have used supervised learning methods (Naive Bayes, SVM) primarily due to their substantial improvements over non-supervised strategies such as traditional clustering in NLP tasks. Until very recently, most NLP classification work was done with the traditional Bag of Words (BOW) approach – perhaps with a bit of context through the use of a limited range of N-grams and skip-grams. BOW is a feature extraction technique where the text is represented as the frequency of each word in the document, disregarding grammar and ordering but keeping multiplicity. In most cases, defining a pipeline combining the BOW feature extraction technique with a Tf-Idf transform and a simple classifier (Naive Bayes, SVM) produces decent results with respect to most classification metrics.

Semi-Supervised Learning with Word2Vec

In most tutorials, Word2Vec is presented as a stand-alone neural net preprocessor for feature extraction. Word2Vec generates a vector for each word in the text corpora in higher-dimensional space such that words that share contextual meaning are located in close proximity to one another. To use Word2Vec for classification, each word can be replaced by its corresponding word vector and usually combined through a naive algorithm such as addition with normalization or cross product to get a sentence or text vector. Then, using these document vectors we could use a simple classifier for multi-label classification. The advantage of using Word2Vec over a simple BOW feature extraction technique is it supports semi-supervised learning, since the vocabulary from the labeled and unlabeled text can be used to generate the word vectors. This allows the words to have more contextual meaning. However, we have found that this approach does not appear to provide significant improvements over a BOW approach especially when there isn’t a lot of labeled data for training the classifier.

Semantic Convolution for Low Support Topics

A common problem that is seen in multi-label text classification is a major imbalance of labels in a textual corpora. We often see cases where most (>60%) of the sampled data is about the most prevalent topic, and more than half the topic labels exist in <0.1% of the sampled data. Almost inherently with NLP and a BOW approach, this causes a p (number of features) >> n (size of training corpus) problem. Based on a general rules of thumb, getting 1,000 training examples for the low support topic would require millions of labeled training examples, which is prohibitively expensive.

In this world of ‘big data’ the data itself is actually cheap, but developing a tagged training set can be expensive. In the course of our development, we devised an elegant and scalable way to develop and maintain a robust training set across tens of industries (this will be the topic of a separate blog post).

The premise of Semantic Convolution is simple: if a particular word is a good indicator of a particular label, then words with similar meanings (semantics) should also be good indicators of the label. Since we have qualitative evidence that Word2Vec vectors encode a semantic meaning, we can use it to help find words with similar meanings from non-labeled corpora. This allows us to apply a Semantic transform after getting the term frequencies in the BOW pipeline, and before applying the Tf-Idf transform. To apply the Semantic Transform, we use the Word2Vec data to generate a correlation matrix between words with similar contextual meaning in the vocabulary.

Given vocabulary is a dictionary mapping each term with an index, the code to generate the correlation_matrix is:

correlation_matrix = scipy.sparse.identity(len(vocabulary), format="dok")
for idx, word in enumerate(vocabulary.keys()):
    similar_words = []

    try:
        similar_words = [x[0] for x in word2vec_model.most_similar(word, topn=5) if x[1] > 0.5]
    except:
        raise

    for similar_word in similar_words:
        if similar_word in vocabulary:
            correlation_matrix[vocabulary[word], vocabulary[similar_word]] = 1

Using this correlation matrix we can generate the term-document matrix with the augmented term frequencies.

term_frequency_vector += term_frequency_vector * correlation_matrix

Applying this transformation with the correlation matrix increases the word count of all words with contextually similar meaning in the text. This improves the feature collection for low support topics, which allows more precise classification of reviews about low support topics with higher confidence. This allows small amounts of labeled data to be more useful for the machine-learning model, which reduces the cost of developing a robust training set. Also, as mentioned above, this leverages semi-supervised learning from the unlabeled data by building the vocabulary and Word2Vec vectors based on the entire text corpora.

Conclusions

Ultimately the Semantic convolution provides more value from the little labeled data, and improves the performance of the machine leaning algorithm for classification tasks, especially the low support categories. Also, semi-supervised learning with Word2Vec leverages the information gained from the vast amounts of unlabeled data while increase both the precision and the support of the machine-learning model.

Dweep Shah and Anthony Johnson


Review Classification with Neural Network Models

Introduction

Extracting meaning from online reviews is key to turn seemingly anecdotal reviews into actionable customer satisfaction insights that point to improvement opportunities or authentic, and potentially differentiating strengths. One way to do that is to apply machine learning to automatically read customer reviews and identify the most relevant topics that are the subject of the review. With this information, you can find themes in what customers are saying about a business across thousands of reviews and then help businesses identify areas in which they are receiving a disproportionate number of negative reviews so that they can focus operational efforts on these areas and improve customer experience as well as their online reputation.

We have been working for a while on several approaches, models, and data sets to extract topics and categories from customer reviews with a high precision. In this post I will give an overview of a few neural network models that provide satisfactory results for physician-related reviews. To start, we built a taxonomy of categories that are relevant to physician reviews looking both at clinical patient experience topics from standard patient assessment surveys designed by CMS (Center for Medicare and Medicaid Services) as well as non-clinical topics related to parking, technology/amenities, and cleanliness that are commonly referred to in physician reviews. Then we gathered training data by having a group of crowd-sourced individuals tag a set of 10,000 reviews with the following categories (this is the subject of an upcoming blog entry):

  • Administrative Process
  • Bedside Manner
  • Cleanliness
  • Competence
  • Getting an Appointment
  • Likely/Unlikely to recommend
  • Parking
  • Responsiveness
  • Staff Courtesy
  • Technology/Amenities
  • Price/Billing issues
  • Wait Time

Given this training data, we used a biologically-inspired variant of Artificial Neural Networks to build a classifier that automatically assigns categories to online physician reviews. These neural network classifiers are based on how an animal’s visual cortex processes and exploits the strong spatially local correlation present in natural images. Those models are generally used for image recognition, but are being increasingly used in other fields, especially text classification. Given the promising results documented in this space, we decided to evaluate Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) with respect to our classification problem.

After an initial trial, we decided to focus our implementation on CNN models as they execute faster, are easier to understand, and had comparable results to RNN.

Principle of CNNs

screen-shot-2016-10-03-at-1-51-58-pmThe starting point of our CNNs was to represent a review by a matrix where each row is a vector that represents a word. This vector could be low-dimensional representations or one-hot vectors that index words into a vocabulary. Given this vector, you can then apply several convolutional filters on groups of rows followed by a 1-max pooling (the largest number from each feature map is recorded) in order to extract the meaning of the group of words considered at the beginning. Finally, a softmax layer is applied to generate assessed probabilities of the review belonging to each class.

CNN Implementation Approach

We generated a CNN model for each category independently broken into two classes: reviews that belong to this category and reviews that do not.

Thus, to produce all of the hidden parameters of these models, we fed them with reviews from the training data that were already categorized and modified the parameters incrementally in order to minimize a loss function (a function that represents the difference between the prediction and the real categories).

CNN Model Effectiveness

To assess the performance of these models, we split the 10,000 reviews into 8,000 reviews for training and 2,000 for testing. Given a model built on the training data, we predicted whether each review in the test data belonged in each category and assessed the precision and recall of our predictions with respect to each category.

For the largest categories, we found that our models delivered an overall precision of 81% and an overall recall of 75%. At first sight, those results did not appear very good. However, when we dug deeper, we found that when we considered at each element of crowd-sourced (human-based) training data to be a prediction in itself, these tags exhibited precision and recall metrics lower than 70% (versus the consensus of the group). Thus, our model outperformed human classification.

Furthermore, looking deeper into the training data, we realized that some reviews were truly ambiguous and the categories not precise or discerning enough, which resulted in a high degree a disagreement between humans evaluating the same review. After removing the most ambiguous reviews from the training data set, we observed a marked increase of the overall accuracy of the model. What to do about those ambiguous reviews or how to fine-tune the categories will be the subject of a future post.

Next Steps

The results from CNN models are promising, and we are pushing them further by experimenting with several modifications of the model such as: oversampling the training set in order to have balanced data for each category, splitting reviews by characters instead of by words, and initializing with a low-dimensional representation of words using Word2Vec. Stay tuned for further updates.