Man pages for adityamangal410/deepSentimentR
Package to Analyze Sentiment Datasets
| bigram_network | Generate bigram network |
| config_params | Constants for the package |
| cor_words | Pairwise correlation of words in given dataset |
| count_bigrams | Count bigrams in given dataset |
| create_conv1d_model | Create 1-Dimensional Convolutional Network model object |
| create_lstm_model | Create LSTM model object |
| freq_by_polarity | Query the dataset for frequency by polarity based on several... |
| generate_embedding_matrix | Generate Embedding Matrix for given word index |
| generate_training_data | Generate training data |
| get_keyword_condition | Make sub-clause for given keywords |
| get_predictions | Get predictions for the given subset of data |
| get_time_condition | Make sub-clause for given time range |
| get_tokenizer | Get Tokenizer for tokenizing train/test data |
| get_user_list_condition | Make sub-clause for given users |
| is_valid_sentiment_data_frame | check if a data frame is valid sentiment data set |
| keras_config_params | Constants for keras model training |
| make_clause | Make clause for given parameters |
| parse_glove_embeddings | Parse glove embeddings |
| predict_polarity_keras | Predict polarity for the given dataset. |
| sentiment140_test | 'sentiment140_test' test dataset from Sentiment140 (<URL:... |
| sentiment140_train | 'sentiment140_train' training dataset from Sentiment140... |
| subsample_input_data | subsample input data to max size |
| time_series | Generate time series plots to analyze data based on filters |
| train_conv_1d | Train 1-dimensional Convolution Network |
| train_conv1d_with_glove | Train 1-dimensional Convolution Network with Glove Embeddings |
| train_lstm | Train LSTM model |
| train_lstm_with_glove | Train LSTM model with Glove Embeddings |
| validate_list | Method to validate a list |
| validate_sentiment_data_frame | Method to validate a data frame |
| validate_time_range | Method to validate input data |
| visualize_bigrams | Generate network plot for bigram and counts |
| visualize_cor | Generate network plot for words and their pairwise... |
| word_cor_network | Generate word correlation network |
