Methods and systems for monitoring and analyzing social media data
View Patent ↗A system and method for analyzing social media data by obtaining social media data from a social media platform, where the social media data includes documents from multiple users of the social media platform; classifying the documents using a sentiment classifier; tokenizing the documents into terms; associating a sentiment with each term; detecting a first event based on a number of occurrences of a first term in the documents; and providing information associated with the event to a user, where the information includes the first term and a sentiment associated with the first term.
1. A method for analyzing social media data, the method comprising:
obtaining, using one or more processors, social media data from a social media platform, wherein the social media data comprises documents from a plurality of users of the social media platform;
classifying the documents using a sentiment classifier;
tokenizing the documents into terms;
associating a sentiment classification with each term;
detecting a first event based on a number of occurrences of a first term in the documents;
providing information associated with the first event to a user, wherein the information comprises the first term and a sentiment classification associated with the first term; and
calculating a term frequency-inverse document frequency (“TFIDF”) metric for the first term, wherein:
the information associated with the first event further comprises the TFIDF metric,
the TFIDF metric is a time normalized TFIDF metric, and
the TFIDF metric is calculated using the formula:
T
F
I
D
F
(
first
term
,
document
,
documents
)
=
∑
document
∈
documents
(
tf
(
term
,
document
)
×
decay
)
×
idf
(
term
,
documents
)
wherein each “document” is a document that included the first term, “tf” is the term frequency, “idf” is the inverse document frequency, and decay is calculated using a timestamp associated with each document.
2. The method of claim 1 , wherein “decay” is calculated using the formula:
decay= e −(current time-document time)
wherein “document time” is determined based on the timestamp associated with each document.
3. The method of claim 1 , wherein the social media data is obtained in batch format.
4. The method of claim 1 , wherein the social media data is obtained in streaming format.
5. The method of claim 1 , wherein detecting the first event is further based on the sentiment classification associated with the first term.
6. The method of claim 5 , wherein the first event is only detected when the first term is associated with a negative sentiment classification.
7. The method of claim 1 , wherein the information is only provided to the user when the first term is associated with a negative sentiment classification.
8. A system for analyzing social media data, the system comprising:
a processing system comprising one or more processors; and
a memory system comprising one or more computer-readable media, wherein the one or more computer-readable media contain instructions that, when executed by the processing system, cause the processing system to perform operations comprising:
obtaining, using one or more processors, social media data from a social media platform, wherein the social media data comprises documents from a plurality of users of the social media platform;
classifying the documents using a sentiment classifier;
tokenizing the documents into terms;
associating a sentiment classification with each term;
detecting a first event based on a number of occurrences of a first term in the documents; and
providing information associated with the first event to a user, wherein the information comprises the first term and a sentiment classification associated with the first term;
calculating a term frequency-inverse document frequency (“TFIDF”) metric for the first term, wherein:
the information associated with the first event further comprises the TFIDF metric,
the TFIDF metric is a time normalized TFIDF metric and
the TFIDF metric is calculated using the formula:
T
F
I
D
F
(
first
term
,
document
,
documents
)
=
∑
document
∈
documents
(
tf
(
term
,
document
)
×
decay
)
×
idf
(
term
,
documents
)
wherein each “document” is a document that included the first term “tf” is the term frequency, “idf” is the inverse document frequency, and decay is calculated using a timestamp associated with each document.
9. The system of claim 8 , wherein “decay” is calculated using the formula:
decay= e −(current time-document time)
wherein “document time” is determined based on the timestamp associated with each document.
10. The system of claim 8 , wherein the social media data is obtained in batch format.
11. The system of claim 8 , wherein the social media data is obtained in streaming format.
12. The system of claim 8 , wherein detecting the first event is further based on the sentiment classification associated with the first term.
13. The system of claim 12 , wherein the first event is only detected when the first term is associated with a negative sentiment classification.
14. The system of claim 8 , wherein the information is only provided to the user when the first term is associated with a negative sentiment classification.