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I am looking for a dictionary that finds the orientation(positive/negative/neutral) of the words as part of analyzing the sentiment of the phrase. Preferably a source that can be imported into python code
You seem to be looking for something like OpinionFinder.
This particular link points to a lexicon of 8233 adjectives, verbs and nouns and their orientation.
You can download it, so you'll be able to simply read the file into python.
From SentiWordNet website:
SentiWordNet is a lexical resource for opinion mining. SentiWordNet
assigns to each synset of WordNet three sentiment scores: positivity,
negativity, objectivity
There are a lot of Python frameworks that use Wordnet and Sentiwordnet, such as NLTK or Pattern.
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I have a dataset with a column containing strings in multiple languages. I am hoping to remove rows where one column contains a string in any language other than English. I can't seem to find any way to go about this. Does anyone have suggestions for a library or code that might be useful for this purpose?
This seems like a repeat of this, as the root of this question is how to detect non-English languages rather than how to filter a dataset.
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Is there any Hierarchical Agglomerative Clustering implementation (in Python) available that preserves the order of data points? For example, I want the output something like this.
(((seg1, seg2), (seg3, seg4)), seg5)
but not like this
(((seg1, seg5), (seg2, seg3)), seg4)
E.g., Actual output with existing implementation
Expected output (any implementation?)
Vijaya, from what I know, there is only one public library that does order preserving hierarchical clustering (ophac), but that will only return a trivial hierarchy if your data is totally ordered (which is the case with the sections of a book).
There is a theory that may offer a theoretical reply to your answer, but no industry-strength algorithms currently exist: https://arxiv.org/abs/2109.04266. I have an implementation of this theory that can deal with up to 20 elements, so if this is interesting, give me a hint, and I will share the code.
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I have been using the Simple Blob Detection algorithm from the OpenCV library (for Python) for a research project. I would like to reference this particular method algorithm in my paper.
Does anyone know from where this method is from and indicate me a good to reference to cite? The openCV source code does not refer to any particular literature.
Thanks
It uses the Connected-component labeling algorithm.
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I'm using LDA to categorize small documents, about 4-5 lines.
I'm categorizing them into topics such as Technology, Politics, Art, Music etc etc
I'm using wikipedia to download articles in each category (Technology, Politics, Art etc etc) and training LDA for each category
Wikipedia is huge (about 8GB compressed), and computations take hours! and uses a huge space in my hard drive
Is there any toolkit that already provides "ready-made" generic topics which i can directly use for categorization?
There are quite a few online API's that categorize text into a predefined set of topics. For example, https://www.textrazor.com/demo identifies topics such as Business, Law, and Politics. You can also take a look at MeaningCloud or AlchemyAPI. Most of these services are paid, but do have a free tier that may be sufficient, depending on your needs.
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Is anyone aware of a pure python implementation of BLAST alignment? I am trying to study this algorithm...
In fact a complete implementation of the BLAST algorithm is a quite hard. It has a lot of steps and optimizations. What could you do is: take a look of the BLAST Book from O'Reilly, for a very good explanation, take a look of the NCBI Blast code base, that it is big and hard to understand at the first glace, or, I sugest you to take a look at other BLAST implementation or may be, others algorithms like BLAT and Genoogle (http://genoogle.pih.bio.br/)
Try looking into BioPython:
http://biopython.org/
http://github.com/JoaoRodrigues/biopython/tree/GSOC2010