Training data for training a neural network usable for electronic sentiment analysis can be automatically constructed. For example, an electronic communication usable for training the neural network and including multiple characters can be received. A sentiment dictionary including multiple expressions mapped to multiple sentiment values representing different sentiments can be received. Each expression in the sentiment dictionary can be mapped to a corresponding sentiment value. An overall sentiment for the electronic communication can be determined using the sentiment dictionary. Training data usable for training the neural network can be automatically constructed based on the overall sentiment of the electronic communication. The neural network can be trained using the training data. A second electronic communication including an unknown sentiment can be received. At least one sentiment associated with the second electronic communication can be determined using the neural network.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A non-transitory computer readable medium comprising program code executable by a processor for causing the processor to: receive an electronic communication usable for training a neural network and comprising a plurality of characters; receive a sentiment dictionary comprising a plurality of expressions mapped to a plurality of sentiment values representing different sentiments, each expression of the plurality of expressions mapped to a corresponding sentiment value of the plurality of sentiment values; determine an overall sentiment for the electronic communication using the sentiment dictionary; automatically construct training data usable for training the neural network based at least in part on the overall sentiment of the electronic communication, wherein the training data comprises a plurality of overall sentiments associated with a plurality of electronic communications usable for training the neural network; train the neural network using the training data; receive a second electronic communication comprising an unknown sentiment; and determine at least one sentiment associated with the second electronic communication using the neural network. 2. The non-transitory computer readable medium of claim 1, further comprising program code executable by the processor for causing the processor to: determine the overall sentiment for the electronic communication using the sentiment dictionary by: segmenting the plurality of characters into a plurality of blocks of characters; determining a total sentiment score for each block of the plurality of blocks of characters using the sentiment dictionary, the total sentiment score representing a sentiment indicated by a block of characters; determining an aggregate sentiment score for the electronic communication by aggregating the total sentiment score for each block of the plurality of blocks of characters; and determining the overall sentiment based on the aggregate sentiment score. 3. The non-transitory computer readable medium of claim 2, further comprising program code executable by the processor for causing the processor to: determine the total sentiment score for each block of the plurality of blocks of characters using the sentiment dictionary by: accessing the sentiment dictionary; identifying one or more expressions in a respective block of characters that are in the sentiment dictionary; mapping the one or more expressions in the respective block to one or more corresponding sentiment values using the sentiment dictionary; and determining a respective total sentiment score for the respective block of characters by aggregating the one or more corresponding sentiment values. 4. The non-transitory computer readable medium of claim 3, further comprising program code executable by the processor for causing the processor to: determine the respective total sentiment score for the respective block of characters by: determining a standard deviation associated with each sentiment value of the one or more corresponding sentiment values; determining weighted sentiment values by multiplying each sentiment value of the one or more corresponding sentiment values by a respective weighting factor based on an associated standard deviation value; and determining the respective total sentiment score for the respective block of characters by aggregating the weighted sentiment values. 5. The non-transitory computer readable medium of claim 2, further comprising program code executable by the processor for causing the processor to: determine the aggregate sentiment score by: determining an average standard deviation associated with each block of the plurality of blocks of characters; determining weighted total sentiment scores by multiplying each total sentiment score by a respective weighting factor based on an associated average standard deviation; and aggregating the weighted total sentiment scores. 6. The non-transitory computer readable medium of claim 2, further comprising program code executable by the processor for causing the processor to: segment the plurality of characters into the plurality of blocks of characters by dividing the plurality of characters such that each block in the plurality of blocks of characters comprises a single sentence including a common sentiment. 7. The non-transitory computer readable medium of claim 1, further comprising program code executable by the processor for causing the processor to: receive the second electronic communication from a text message, an e-mail, a social media post, a tweet, a blog post, or a forum post. 8. The non-transitory computer readable medium of claim 1, further comprising program code executable by the processor for causing the processor to: display a graphical user interface (GUI) visually indicating the at least one sentiment associated with the second electronic communication, the at least one sentiment comprising a positive sentiment, a neutral sentiment, or a negative sentiment. 9. The non-transitory computer readable medium of claim 8, wherein the GUI comprises a graph indicating at least one transition between two or more different sentiments associated with the second electronic communication. 10. The non-transitory computer readable medium of claim 8, further comprising program code executable by the processor for causing the processor to: determine a provider of the at least one sentiment associated with the second electronic communication; and indicate the provider within the GUI. 11. A method comprising: receiving an electronic communication usable for training a neural network and comprising a plurality of characters; receiving a sentiment dictionary comprising a plurality of expressions mapped to a plurality of sentiment values representing different sentiments, each expression of the plurality of expressions mapped to a corresponding sentiment value of the plurality of sentiment values; determining an overall sentiment for the electronic communication using the sentiment dictionary; automatically constructing training data usable for training the neural network based at least in part on the overall sentiment of the electronic communication, wherein the training data comprises a plurality of overall sentiments associated with a plurality of electronic communications usable for training the neural network; training the neural network using the training data; receiving a second electronic communication comprising at least one unknown sentiment; and determining at least one sentiment associated with the second electronic communication using the neural network. 12. The method of claim 11, further comprising: determining the overall sentiment for the electronic communication using the sentiment dictionary by: segmenting the plurality of characters into a plurality of blocks of characters; determining a total sentiment score for each block of the plurality of blocks of characters using the sentiment dictionary, the total sentiment score representing a sentiment indicated by a block of characters; and determining an aggregate sentiment score for the electronic communication by aggregating the total sentiment score for each block of the plurality of blocks of characters; and determining the overall sentiment based on the aggregate sentiment score. 13. The method of claim 12, further comprising: determining the total sentiment score for each block of the plurality of blocks of characters using the sentiment dictionary by: accessing the sentiment dictionary; identifying one or more expressions in a respective block of characters that are in the sentiment dictionary; mapping the one or more expressions in the respective block to one or more corresponding sentiment values using the sentiment dictionary; and determining a respective total sentiment score for the respective block of characters by aggregating the one or more corresponding sentiment values. 14. The method of claim 13, further comprising: determining the respective total sentiment score for the respective block of characters by: determining a standard deviation associated with each sentiment value of the one or more corresponding sentiment values; determining weighted sentiment values by multiplying each sentiment value of the one or more corresponding sentiment values by a respective weighting factor based on an associated standard deviation value; and determining the respective total sentiment score for the respective block of characters by aggregating the weighted sentiment values. 15. The method of claim 12, further comprising: determining the aggregate sentiment score by: determining an average standard deviation associated with each block of the plurality of blocks of characters; determining weighted total sentiment scores by multiplying each total sentiment score by a respective weighting factor based on an associated average standard deviation; and aggregating the weighted total sentiment scores. 16. The method of claim 12, further comprising: segmenting the plurality of characters into the plurality of blocks of characters by dividing the plurality of characters such that each block in the plurality of blocks of characters comprises a single sentence including a common sentiment. 17. The method of claim 11, further comprising: receiving the second electronic communication from a text message, an e-mail, a social media post, a tweet, a blog post, or a forum post. 18. The method of claim 11, further comprising: displaying a graphical user interface (GUI) visually indicating the at least one sentiment associated with the second electronic communication, the at least one sentiment comprising a positive sentiment, a neutral sentiment, or a negative sentiment. 19. The method of claim 18, wherein the GUI comprises a graph indicating at least one transition between two or more different sentiments associated with the second electronic communication. 20. The method of claim 18, further comprising: determining a provider of the at least one sentiment associated with the second electronic communication; and indicating the provider within the GUI. 21. A system comprising: a processing device; and a memory device in which instructions executable by the processing device are stored for causing the processing device to: receive an electronic communication usable for training a neural network and comprising a plurality of characters; receive a sentiment dictionary comprising a plurality of expressions mapped to a plurality of sentiment values representing different sentiments, each expression of the plurality of expressions mapped to a corresponding sentiment value of the plurality of sentiment values; determine an overall sentiment for the electronic communication using the sentiment dictionary; automatically construct training data usable for training the neural network based at least in part on the overall sentiment of the electronic communication, wherein the training data comprises a plurality of overall sentiments associated with a plurality of electronic communications usable for training the neural network; train the neural network using the training data; receive a second electronic communication comprising at least one unknown sentiment; and determine at least one sentiment associated with the second electronic communication using the neural network. 22. The system of claim 21, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: determine the overall sentiment for the electronic communication using the sentiment dictionary by: segmenting the plurality of characters into a plurality of blocks of characters; determining a total sentiment score for each block of the plurality of blocks of characters using the sentiment dictionary, the total sentiment score representing a sentiment indicated by a block of characters; and determining an aggregate sentiment score for the electronic communication by aggregating the total sentiment score for each block of the plurality of blocks of characters; and determining the overall sentiment based on the aggregate sentiment score. 23. The system of claim 22, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: determine the total sentiment score for each block of the plurality of blocks of characters using the sentiment dictionary by: accessing the sentiment dictionary; identifying one or more expressions in a respective block of characters that are in the sentiment dictionary; mapping the one or more expressions in the respective block to one or more corresponding sentiment values using the sentiment dictionary; and determining a respective total sentiment score for the respective block of characters by aggregating the one or more corresponding sentiment values. 24. The system of claim 23, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: determine the respective total sentiment score for the respective block of characters by: determining a standard deviation associated with each sentiment value of the one or more corresponding sentiment values; determining weighted sentiment values by multiplying each sentiment value of the one or more corresponding sentiment values by a respective weighting factor based on an associated standard deviation value; and determining the respective total sentiment score for the respective block of characters by aggregating the weighted sentiment values. 25. The system of claim 22, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: determine aggregate sentiment score by: determining an average standard deviation associated with each block of the plurality of blocks of characters; determining weighted total sentiment scores by multiplying each total sentiment score by a respective weighting factor based on an associated average standard deviation; and aggregating the weighted total sentiment scores. 26. The system of claim 22, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: segment the plurality of characters into the plurality of blocks of characters by dividing the plurality of characters such that each block in the plurality of blocks of characters comprises a single sentence including a common sentiment. 27. The system of claim 21, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: receive the second electronic communication from a text message, an e-mail, a social media post, a tweet, a blog post, or a forum post. 28. The system of claim 21, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: display a graphical user interface (GUI) visually indicating the at least one sentiment associated with the second electronic communication, the at least one sentiment comprising a positive sentiment, a neutral sentiment, or a negative sentiment. 29. The system of claim 28, wherein the GUI comprises a graph indicating at least one transition between two or more different sentiments associated with the second electronic communication. 30. The system of claim 28, wherein the memory device further comprises instructions executable by the processing device for causing the processing device to: determine a provider of the at least one sentiment associated with the second electronic communication; and indicate the provider within the GUI.