Hi i want to implement a system which can identify whether the given sentence is an incident or a sentiment.
I was going through python NLTK and found out that there is a way to find out positivity or negativity of a sentense.
Found out the ref link: ref link
I want to achieve like
My new Phone is not as good as I expected should be treated as sentiment
and Camera of my phone is not working should be considered as incident.
I gave a Idea of making my own clusters for training my system for finding out such but not getting a desired solution is there a built-in way to find that or any idea on how can be approach for solution of same.
Advance thanks for your time.
If you have, or can construct, a corpus of appropriately categorized sentences, you could use it to train a classifier. There can be as many categories as you need (two, three or more).
You'll have to do some work (reading and experimenting) to find the best features to use for the task. I'd start by POS-tagging the sentence so you can pull out the verb(s), etc. Take a look at the NLTK book's chapter on classifiers.
Use proper training/testing methodology (always test on data that was not seen during training), and make sure you have enough training data-- it's easy to "overtrain" your classifier so that it does well on the training data, by using characteristics that coincidentally correlate with the category but will not recur in novel data.
Related
First, I want to explain my task. I have a dataset of 300k documents with an average of 560 words (no stop word removal yet) 75% in German, 15% in English and the rest in different languages. The goal is to recommend similar documents based on an existing one. At the beginning I want to focus on the German and English documents.
To achieve this goal I looked into several methods on feature extraction for document similarity, especially the word embedding methods have impressed me because they are context aware in contrast to simple TF-IDF feature extraction and the calculation of cosine similarity.
I'm overwhelmed by the amount of methods I could use and I haven't found a proper evaluation of those methods yet. I know for sure that the size of my documents are too big for BERT, but there is FastText, Sent2Vec, Doc2Vec and the Universal Sentence Encoder from Google. My favorite method based on my research is Doc2Vec even though there aren't any or old pre-trained models which means I have to do the training on my own.
Now that you know my task and goal, I have the following questions:
Which method should I use for feature extraction based on the rough overview of my data?
My dataset is too small to train Doc2Vec on it. Do I achieve good results if I train the model on English / German Wikipedia?
You really have to try the different methods on your data, with your specific user tasks, with your time/resources budget to know which makes sense.
You 225K German documents and 45k English documents are each plausibly large enough to use Doc2Vec - as they match or exceed some published results. So you wouldn't necessarily need to add training on something else (like Wikipedia) instead, and whether adding that to your data would help or hurt is another thing you'd need to determine experimentally.
(There might be special challenges in German given compound words using common-enough roots but being individually rare, I'm not sure. FastText-based approaches that use word-fragments might be helpful, but I don't know a Doc2Vec-like algorithm that necessarily uses that same char-ngrams trick. The closest that might be possible is to use Facebook FastText's supervised mode, with a rich set of meaningful known-labels to bootstrap better text vectors - but that's highly speculative and that mode isn't supported in Gensim.)
I have ~138k records of user feedback that I'd like to analyze to understand broad patterns in what our users are most often saying. Each one has a rating between 1-5 stars, so I don't need to do any sort of sentiment analysis. I'm mostly interested in splitting the dataset into >=4 stars to see what we're doing well and <= 3 stars to see what we need to improve upon.
One key problem I'm running into is that I expect to see a lot of n-grams. Some of these I know, like "HOV lane", "carpool lane", "detour time", "out of my way", etc. But I also want to detect common bi- and tri-grams programmatically. I've been playing around with Spacy a bit, but it doesn't seem to have any capability to do analysis on the corpus level, only on the document level.
Ideally my pipeline would look something like this (I think):
Import a list of known n-grams into the tokenizer
Process each string into a tokenized document, removing punctuation,
stopwords, etc, while respecting the known n-grams during
tokenization (ie, "HOV lane" should be a single noun token)
Identify the most common bi- and tri- grams in the corpus that I
missed
Re-tokenize using the found n-grams
Split by rating (>=4 and <=3)
Find the most common topics for each split of data in the corpus
I can't seem to find a single tool, or even a collection of tools, that will allow me to do what I want here. Am I approaching this the wrong way somehow? Any pointers on how to get started would be greatly appreciated!
Bingo State of the art results for your problem!
Its called - Zero-Short learning.
State-of-the-art NLP models for text classification without annotated data.
For Code and details read the blog - https://joeddav.github.io/blog/2020/05/29/ZSL.html
Let me know if it works for you or for any other help.
VADER tool is perfect with sentiment analysis and NLP based applications.
I think the proposed workflow is fine with this case study. Closely work with your feature extraction as it matters a lot.
Most of the time tri-grams make a sound sense on these use cases.
Using Spacy would be a better decision as SpaCy's rules-based match engines and components not only help you to find what the terms and sentences are searching for but also allow you to access the tokens inside a text and its relationships compared with regular expressions.
I have been using spacy to find the NER of sentences.My problem is I have to calculate the NER similarity between sentences of two different documents. Is there any formula or package available in python for the same?
TIA
I believe you are asking, how similar are two named entities?
This is not so trivial, since we have to define what "similar" means.
If we use a naive bag-of-words approach, two entities are more similar when more of their tokens are identical.
If we put the entity tokens into sets, the calculation would just be the jaccard coefficient.
Sim(ent1, ent2) = |ent1 ∩ ent2| / |ent1 ∪ ent2|
Which in python would be:
ent1 = set(map(str, spacy_entity1))
ent2 = set(map(str, spacy_entity2))
similarity = len(ent1 & ent2) / len(ent1 | ent2)
Where spacy_entity is one of the entities extracted by spacy
We then just create entity sets ent by creating a set of the strings that represent them.
I've been tackling the same problem and heres how I'm Solving.
Since youve given no context about your problem, ill keep the solution as general as possible
Links to tools:
Spacy Spacy Similarity
Flair NLP: Flair NLP zero shot few shot
Roam Research for research articles and their approach Roam Research home page
DISCLAIMER: THIS IS A TRIAL EXPERIMENT. AND NOT THE SOLUTION
Steps:
try a Large language model with a similarity coefficient (Like GPT-3 or TARS) on your dataset
(Instead of similarity you could also use the zero shot/few shot classification and then manually see the accuracy or compute it if you have labelled data)
I am then grab the verbs (assuming you have a corpus and not single word inputs) and calculating their similariy (attaching x-n and x+n words [exlude stopwords according to your domain] if x is position of verb)
This step mainly allows you give more context to the large language model so it is not biased on its large corpus (this is experimental)
And finally Im grabbing all the named entities and hopefully their labels (Example India: Country/Location) And Ask the same LLM (Large Language model) after youve constructed its Prompt to see how many of the same buckets/categories your entities fall into (probability comparison maybe)
Even if you dont reproduce these steps, what you must understand is that these tools give you atomic information about your raw input. you have to put mathematic functions to compare and make the algorithm
In my case im averaging all 3 similarity index of the above steps and ensuring that all classification is multilabel classification
And to validate any of this, human pattern matching maybe.
You need probably http://uima.apache.org/d/uimacpp-2.4.0/docs/Python.html/ plus a CoNLL-U parser attached to it https://universaldependencies.org/format.html. With this approach NERs are based to a dictionary in UIMA Pipeline. You need to develop some proprietary NER search/match algorithms (in Python or in other supported language).
I have a dataset that would be equivalent to a dict of 5 millions key-values, both strings.
Each key is unique but there are only a couple hundreds of different values.
Keys are not natural words but technical references. The values are "families", grouping similar technical references. Similar is meant in the sense of "having similar regex", "including similar characters", or some sort of pattern.
Example of key-values:
ADSF33344 : G1112
AWDX45603 : G1112
D99991111222 : X3334
E98881188393 : X3334
A30-00005-01 : B0007
B45-00234-07A : B0007
F50-01120-06 : B0007
The final goal is to feed an algorithm with a list of new references (never seen before) and the algorithm would return a suggested family for each reference, ideally together with a percentage of confidence, based on what it learned from the dataset.
The suggested family can only come from the existing families found in the dataset. No need to "invent" new family name.
I'm not familiar with machine learning so I don't really know where to start. I saw some solutions through Sklearn or TextBlob and I understand that I'm looking for a classifier algorithm but every tutorial is oriented toward analysis of large texts.
Somehow, I don't find how to handle my problem, although it seems to be a "simpler" problem than analysing newspaper articles in natural language...
Could you indicate me sources or tutorials that could help me?
Make a training dataset, and train a classifier. Most classifiers work on the values of a set of features that you define yourself. (The kind of features depends on the classifier; in some cases they are numeric quantities, in other cases true/false, in others they can take several discrete values.) You provide the features and the classifier decides how important each feature is, and how to interpret their combinations.
By way of a tutorial you can look at chapter 6 of the NLTK book. The example task, the classification of names into male and female, is structurally very close to yours: Based on the form of short strings (names), classify them into categories (genders).
You will translate each part number into a dictionary of features. Since you don't show us the real data, nobody give you concrete suggestions, but you should definitely make general-purpose features as in the book, and in addition you should make a feature out of every clue, strong or weak, that you are aware of. If supplier IDS differ in length, make a length feature. If the presence (or number or position) of hyphens is a clue, make that into a feature. If some suppliers' parts use a lot of zeros, ditto. Then make additional features for anything else, e.g. "first three letters" that might be useful. Once you have a working system, experiment with different feature sets and different classifier engines and algorithms, until you get acceptable performance.
To get good results with new data, don't forget to split up your training data into training, testing and evaluation subsets. You could use all this with any classifier, but the NLTK's Naive Bayes classifier is pretty quick to train so you could start with that. (Note that the features can be discrete values, e.g. first_letter can be the actual letter; you don't need to stick to boolean features.)
I want to classify the tweets into predefined categories (like: sports, health, and 10 more). If I had labeled data, I would be able to do the classification by training Naive Bayes or SVM. As described in http://cucis.ece.northwestern.edu/publications/pdf/LeePal11.pdf
But I cannot figure out a way with unlabeled data. One possibility could be using Expectation-Maximization and generating clusters and label those clusters. But as said earlier I have predefined set of classes, so clustering won't be as good.
Can anyone guide me on what techniques I should follow. Appreciate any help.
Alright by what i can understand i think there are multiple ways to attend to this case.
there will be trade offs and the accuracy rate may vary. because of the well know fact and observation
Each single tweet is distinct!
(unless you are extracting data from twitter stream api based on tags and other keywords). Please define the source of data and how are you extracting it. i am assuming you're just getting general tweets which can be about anything
The thing you can do is to generate a set of dictionary for each class you have
(i.e Music => pop , jazz , rap , instruments ...)
which will contain relevant words to that class. You can use NLTK for python or Stanford NLP for other languages.
You can start with extracting
Synonyms
Hyponyms
Hypernyms
Meronyms
Holonyms
Go see these NLP Lexical semantics slides. it will surely clear some of the concepts.
Once you have dictionaries for each classes. cross compare them with the tweets you have got. the tweet which has the most similarity (you can rank them according to the occurrences of words from the these dictionaries) you can label it to that class. This will make your tweets labeled like others.
Now the question is the accuracy! But it depends on the data and versatility of your classes. This may be an "Over kill" But it may come close to what you want.
Furthermore you can label some set of tweets this way and use Cosine Similarity to cross identify other tweets. This will help with the optimization part. But then again its up-to you. As you know what Trade offs you can bear
The real struggle will be the machine learning part and how you manage that.
Actually this seems as a typical use case of semi-supervised learning. There are plenty methods of use here, including clustering with constraints (where you force model to cluster samples from the same class together), transductive learning (where you try to extrapolate model from labeled samples onto distribution of unlabeled ones).
You could also simply cluster data as #Shoaib suggested, but then you will have to come up the the heuristic approach how to deal with clusters with mixed labeling. Futhermore - obviously solving optimziation problem not related to the task (labeling) will not be as good as actually using this knowledge.
You can use clustering for that task. For that you have to label some examples for each class first. Then using these labeled examples, you can identify the class of each cluster easily.