I have a JSON file (tweets.json) that contains tweets (sentences) along with the name of the author. Objective 1: Get the most frequent entities from the tweets. Objective 2: Find out the sentiment/polarity of each author towards each of the entities. Sample Input: Assume we have only 3 tweets: Tweet1 by Author1: Pink Pearl Apples are tasty but Empire Apples are not. Tweet2 by Author2: Empire Apples are very tasty. Tweet3 by Author3: Pink Pearl Apples are not tasty. Sample …

I stumbled upon different sources that state that each sentence starts with a CLS token when passed to BERT. I'm passing text documents with multiple sentences to BERT. This would mean that for each sentence, I would have one CLS token. Pooled output is however only returning a vector of size hidden state. Does this mean that all CLS tokens are somehow compressed to one (averaging?)? Or does my text document only contain one single CLS token for the whole …

I am working on Sentiment Analysis model. The dataset I have has three labels: positive, negative and neutral. But the problem is the data is not equal for labels. Say out of 100K : 75 K are neutral, 15K positive and 10K negative. I wanted to know whether it is necessary to choose equal distribution of labels while training or I can go ahead with unequal data and if so till what extent? Are there any ways to deal with …

I want to make sentiment analysis for an entity which was found, like Google NLP. Entity should have magnitude and score. Please share with me the possible research papers. p/s please not propose to make sentiment for sentence where the entity is located and them assign to entity from such sentence.

I have trained a classifier algorithm on a sentiment analysis model which classifies the reviews scraped off Amazon as Positive or Negative. Now for each class, I want to get the keywords from the review i.e. reason for the positive or negative review. For example if I have a review "the quality of the shirt is the worst!". I want to get the keyword as "quality". Similarly "Really liked the fitting of the shirt" should return "fitting" as the keyword. …

I would like to build a simple sentiment analysis classifier using logistic regression. I downloaded a list of positive and negative words from cs.uic.edu. There are more than 6000 words both positive and negative. Linear Classifier has the form: (Wikipedia Reference) $$\sum wj*xj$$ where $w$ is the weight of the vector $x$. So for example, if the weight of vector awesome is 3, then in the following sentence: Food is awesome and music is awesome. according to the formula, it …

I'm trying to implement the unsupervised k-means algorithm for sentiment analysis of imdb movie dataset created by stanford. The steps that I followed is : 1) Load the comments 2) Apply tokenization and stemmetion ,use tf-idf algo to create tfidf matrix. 3) Use k-means algo to divide the data into 2 clusters. My problem is how do I validate the the clusters I have labeled test data. I want to check if all the negative examples go in one cluster …

I am trying to find a model or way to classify text which falls into a category and its a positive or negative feedback. For ex. we have three columns Review : Camera's not good battery backup is not very good. Ok ok product camera's not very good and battery backup is not very good. Rating : 2 Topic :['Camera (Neutral)', 'Battery (Neutral)'] My Whole Dataset is like above and Topic is not standard one , Topic value is based …

I have a dataset of different sentences from news articles which I need to classify by their sentiment. For that goal I'm planning to use a fine-tuned model which was fine-tuned on different datasets, for example various comments from forums, reviews, tweets. However, news articles are supposedly quite different from that dataset as they are usually more neutral. I understand that a correct way to approach this issue would be by training a model on my own labeled dataset, however …

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 …

I was reading articles on sentiment analysis and NLP and there is something I cant quite understand. One of the methods to label a dataset is to use something like textblob with a polarity dictionary that would count words in a positive and negative dictionary and give a score based on it. Then the dataset is used to train a classification algorithm. My question is, why do we bother with ML at all while we have a rule-based labeling method …

X_train has only one column that contains all tweets. xlnet_model = 'xlnet-large-cased' xlnet_tokenizer = XLNetTokenizer.from_pretrained(xlnet_model) def get_inputs(tweets, tokenizer, max_len=120): """ Gets tensors from text using the tokenizer provided""" inps = [tokenizer.encode_plus(t, max_length=max_len, pad_to_max_length=True, add_special_tokens=True) for t in tweets] inp_tok = np.array([a['input_ids'] for a in inps]) ids = np.array([a['attention_mask'] for a in inps]) segments = np.array([a['token_type_ids'] for a in inps]) return inp_tok, ids, segments inp_tok, ids, segments = get_inputs(X_train, xlnet_tokenizer) AttributeError: 'NoneType' object has no attribute 'encode_plus'

I'm doing sentiment analysis of tweets related to recent acquisition of Twitter by Elon Musk. I have a corpus of 10 000 tweets and I'd like to use machine learning methods using models like SVM and Linear Regression. My question is, when I want to train the models, do I have to manually tag big portion of those 10 000 collected tweets with either positive or negative class to train the model correctly or can I use some other dataset …

Hei! I want to train a model, that predicts the sentiment of news headlines. I've got multiple unordered news headlines per day, but one sentiment score. What is a convenient solution to overcome the not 1:1 issue? I could: Concatenate all headlines to one string, but that feels a bit wrong, as an LSTM or CNN will use cross-sentence word relations, that don't exist. Predict one score per headline (1:1), and take the average in the application. But that might …

I am using owl ontology for semantic analysis in emotional sentiment analysis project , I am trying to navigate the ontology to check a concepts and its relation , my ontology has classes like this : <!-- http://purl.obolibrary.org/obo/MFOEM_000011 --> <owl:Class rdf:about="http://purl.obolibrary.org/obo/MFOEM_000011"> <rdfs:subClassOf rdf:resource="http://purl.obolibrary.org/obo/MFOEM_000001" /> <rdfs:subClassOf> <owl:Restriction> <owl:onProperty rdf:resource="http://purl.obolibrary.org/obo/BFO_0000117" /> <owl:someValuesFrom rdf:resource="http://purl.obolibrary.org/obo/MFOEM_000208" /> </owl:Restriction> </rdfs:subClassOf> <obo:IAO_0000115>An unpleasant emotion closely related to anger but lower in intensity and without the moral dimension of blame and seriousness that is implicated in anger. [Source: …

My data includes women's comments on X and Y and men's comments on X and Y. Each comment is of equal length. I will calculate how much different the word choice between men and women when commenting on X. How can it do this?

This Keras article / tutorial here does perform text standardization i.e removing HTML elements, punctuation, etc. from the text dataset, however, there is a distinct lack of any stemming or lemmatization before the vectorization step. I have a bit of experience in deep learning but I am very new to NLP, and I just got to know (from a different tutorial on Udemy, which BTW was using Bag of Words) that using either a Stemmer or a Lemmatizer helps in …

I want to do a classification through comments categorized in 4 areas(X,Y,Z,M). Categorizing the product as good or bad based on the comments in the fields X, Y, Z, M. How can I follow a path to see the effects of these 4 areas on the result. For example; Id X Y Z M Result 1 The prod.. I fell.. Very bad.. lost of time.. 0(bad) Using this data, the model will be given comments in the x, y, z, …

I'm doing sentiment analysis on a twitter dataset (problem link). I have extracted the POS tags from the tweets and created tfidf vectors from the POS tags and used them as a feature (got accuracy of 65%). But I think, we can achieve a lot more with POS tags since they help to distinguish how a word is being used within the scope of a phrase. The model I'm training is MultnomialNB(). The problem I'm trying to solve is to …

I'm using a reviews data and trying to apply classifier model and get prediction. Here is the code i'm trying. dataset = pd.read_csv('Scraping reviews.csv') import numpy as np X = np.linspace(0, 2*np.pi, 8) y = np.sin(X) + np.random.normal(0, 0.4, 8) X = X.reshape(-1, 1) from sklearn.feature_extraction.text import CountVectorizer count_vect = CountVectorizer() X_train_counts = count_vect.fit_transform(dataset) #X_train_counts=X_train_counts.reshape(4,1) X_train_counts.shape [out] (2,2) from sklearn.feature_extraction.text import TfidfTransformer tf_transformer = TfidfTransformer(use_idf=False).fit(X_train_counts) X_train_tf = tf_transformer.transform(X_train_counts) #X_train_tf=X_train_tf.reshape(4,1) X_train_tf.shape [out] (2,2) tfidf_transformer = TfidfTransformer() X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts) #X_train_tfidf=X_train_tfidf.reshape(4,1) X_train_tfidf.shape …